{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/vbove/Dropbox/terror, migrants, civil war/VT-Analysis/Summary Final Analysis 4.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}26 Mar 2015, 11:06:31
{txt}
{com}. 
. set matsize 8000
{txt}
{com}. use "Data Set I.dta", clear 
{txt}
{com}. 
. ******************************
. * Data Setup - Spatial Lags  *
. ******************************
. 
. spatwmat using "Contiguity Matrix.dta", name(W)standardize


{txt}The following matrix has been created:

1. Imported binary weights matrix {res}W{txt} (row-standardized)
   Dimension: {res}3919x3919

{err}   Beware! 497 locations have no neighbors


{txt}
{com}. spatgsa LDV1, w(W) m twotail
{res}

{txt}{title:Measures of global spatial autocorrelation}


Weights matrix
{hline 62}
Name: {res}W
{txt}Type: {res}Imported (binary)
{txt}Row-standardized: {res}Yes
{txt}{hline 62}

Moran's I
{hline 20}{c TT}{hline 41}
{col 11}Variables {c |}{col 26}I{col 33}E(I){col 40}sd(I){col 50}z{col 55}p-value*
{hline 20}{c +}{hline 41}
{col 1}               LDV1 {c |}{res}{col 22}  0.361{col 30} -0.000{col 38}  0.015{col 46} 24.174{col 56}0.000
{txt}{hline 20}{c BT}{hline 41}
*2-tail test



{com}. splagvar LDV1, wname(W) wfrom(Stata)
{res}
Spatially lagged variable(s) calculated successfully and/or all requests processed.
{txt}
{com}. rename wy_LDV1 wy_contiguity
{res}{txt}
{com}. 
. spatwmat using  "Inv_Distance Matrix.dta", name(W)standardize


{txt}The following matrix has been created:

1. Imported non-binary weights matrix {res}W{txt} (row-standardized)
   Dimension: {res}3919x3919


{txt}
{com}. spatgsa LDV1, w(W) m twotail
{res}

{txt}{title:Measures of global spatial autocorrelation}


Weights matrix
{hline 62}
Name: {res}W
{txt}Type: {res}Imported (non-binary)
{txt}Row-standardized: {res}Yes
{txt}{hline 62}

Moran's I
{hline 20}{c TT}{hline 41}
{col 11}Variables {c |}{col 26}I{col 33}E(I){col 40}sd(I){col 50}z{col 55}p-value*
{hline 20}{c +}{hline 41}
{col 1}               LDV1 {c |}{res}{col 22}  0.155{col 30} -0.000{col 38}  0.002{col 46} 74.538{col 56}0.000
{txt}{hline 20}{c BT}{hline 41}
*2-tail test



{com}. splagvar LDV1, wname(W) wfrom(Stata)
{res}
Spatially lagged variable(s) calculated successfully and/or all requests processed.
{txt}
{com}. rename wy_LDV1 wy_invdistance
{res}{txt}
{com}. 
. spatwmat using "Matrix.dta", name(W)standardize


{txt}The following matrix has been created:

1. Imported non-binary weights matrix {res}W{txt} (row-standardized)
   Dimension: {res}3919x3919

{err}   Beware! 1 location has no neighbors


{txt}
{com}. spatgsa LDV1, w(W) m twotail
{res}

{txt}{title:Measures of global spatial autocorrelation}


Weights matrix
{hline 62}
Name: {res}W
{txt}Type: {res}Imported (non-binary)
{txt}Row-standardized: {res}Yes
{txt}{hline 62}

Moran's I
{hline 20}{c TT}{hline 41}
{col 11}Variables {c |}{col 26}I{col 33}E(I){col 40}sd(I){col 50}z{col 55}p-value*
{hline 20}{c +}{hline 41}
{col 1}               LDV1 {c |}{res}{col 22}  0.315{col 30} -0.000{col 38}  0.010{col 46} 30.304{col 56}0.000
{txt}{hline 20}{c BT}{hline 41}
*2-tail test



{com}. splagvar LDV1, wname(W) wfrom(Stata)
{res}
Spatially lagged variable(s) calculated successfully and/or all requests processed.
{txt}
{com}. rename wy_LDV1 wy_migrants
{res}{txt}
{com}. 
. ************************
. * Descriptive Overview *
. ************************
. 
. kdensity ln_FGTD
{res}{txt}
{com}. sum ln_FGTD geddes1-geddes5 logGNI logpop logarea GINI Durable AggSF ColdWar ucdp_type2 ucdp_type3 log_iMigrantsBA LDV1 

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 5}ln_FGTD {c |}{res}      3919    1.104985    1.493938          0   6.658011
{txt}{space 5}geddes1 {c |}{res}      3919    .1388109    .3457932          0          1
{txt}{space 5}geddes2 {c |}{res}      3919    .0819087    .2742605          0          1
{txt}{space 5}geddes3 {c |}{res}      3919    .2477673     .431771          0          1
{txt}{space 5}geddes4 {c |}{res}      3919    .0632814    .2434996          0          1
{txt}{hline 13}{c +}{hline 56}
{space 5}geddes5 {c |}{res}      3919    .0329166     .178441          0          1
{txt}{space 6}logGNI {c |}{res}      3919    7.177237    1.600371   3.919197   14.10218
{txt}{space 6}logpop {c |}{res}      3919    2.021224    1.595263  -2.120264   7.145866
{txt}{space 5}logarea {c |}{res}      3919    12.17269    1.963643   5.755742   16.64848
{txt}{space 8}GINI {c |}{res}      3919    44.30786    8.496184       17.8         72
{txt}{hline 13}{c +}{hline 56}
{space 5}Durable {c |}{res}      3919    23.08114    28.18199          0        191
{txt}{space 7}AggSF {c |}{res}      3919    .6091095    1.665247          0       13.5
{txt}{space 5}ColdWar {c |}{res}      3919    .6522072    .4763306          0          1
{txt}{space 2}ucdp_type2 {c |}{res}      3919    .0972187    .5021545          0          3
{txt}{space 2}ucdp_type3 {c |}{res}      3919    .3141107    .7998101          0          3
{txt}{hline 13}{c +}{hline 56}
log_iMigra~A {c |}{res}      3919    11.73814    1.736675          0   17.20026
{txt}{space 8}LDV1 {c |}{res}      3919    1.086771    1.489279          0   6.658011
{txt}
{com}. sum wy_contiguity wy_invdistance wy_migrants

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
wy_contigu~y {c |}{res}      3919    1.146182    1.271721          0   6.216606
{txt}wy_invdist~e {c |}{res}      3919    1.070945    .5838868    .223752   3.321317
{txt}{space 1}wy_migrants {c |}{res}      3919    1.624887    1.202954          0   5.849319
{txt}
{com}. 
. ***********************************
. * Model Estimation 1 - Contiguity *
. ***********************************
. 
. sort year ccode
{txt}
{com}. 
. clear
{txt}
{com}. pr drop _all
{txt}
{com}. set matsize 8000
{txt}
{com}. set more off
{txt}
{com}. version 9
{txt}
{com}. 
. *  Likelihood Evaluator *                                                          
. 
. program define splag_ll
{txt}  1{com}. 
. args lnf mu rho sigma
{txt}  2{com}. tempvar A rSL
{txt}  3{com}. gen `A'= ones - `rho'*EIGS1
{txt}  4{com}. gen `rSL'=`rho'*SL1
{txt}  5{com}. qui replace `lnf'= ln(`A') + ln(normden($ML_y1-`rSL'-`mu', 0, `sigma'))
{txt}  6{com}. end
{txt}
{com}. 
. global nobs = 3919
{txt}
{com}. 
. * Open Data For Weights *
. 
. clear
{txt}
{com}. spatwmat using "Contiguity Matrix.dta", name(W) standardize


{txt}The following matrix has been created:

1. Imported binary weights matrix {res}W{txt} (row-standardized)
   Dimension: {res}3919x3919

{err}   Beware! 497 locations have no neighbors


{txt}
{com}. matrix eigenvalues eig1 imaginaryv = W
{txt}
{com}. matrix eig2 = eig1'
{txt}
{com}. matrix ones=J($nobs,1,1)
{txt}
{com}. 
. * Open Data for Regression *
. 
. drop _all
{txt}
{com}. 
. use "Data Set I.dta", clear
{txt}
{com}. 
. global Y ln_FGTD
{txt}
{com}. global X geddes1-geddes5 logGNI logpop logarea GINI Durable AggSF ColdWar ucdp_type2 ucdp_type3 log_iMigrantsBA LDV1 country_fe2-country_fe145 year_fe2-year_fe31
{txt}
{com}. global Z LDV1
{txt}
{com}. mkmat $Y, matrix(Y)
{res}{txt}
{com}. mkmat $Z, matrix(Z)
{res}{txt}
{com}. matrix SL = W*Z
{txt}
{com}. svmat SL, n(SL)
{txt}
{com}. svmat eig2, n(EIGS)
{txt}
{com}. svmat ones, n(ones)
{txt}
{com}. 
. * Produce Starting Values * 
. 
. qui regress $Y $X
{txt}
{com}. matrix OLSb=e(b)
{txt}
{com}. local OLSsigma=e(rmse)
{txt}
{com}. 
. * Estimate Spatial Lag Model *
. 
. ml model lf splag_ll (mu: $Y=$X) (rho:) (sigma:), technique(dfp) vce(oim)
{txt}note: country_fe138 dropped because of collinearity
note: year_fe31 dropped because of collinearity

{com}. ml init OLSb, skip
{txt}
{com}. ml init rho:_cons=0
{txt}
{com}. ml init sigma:_cons=`OLSsigma'
{txt}
{com}. ml max, difficult ltolerance(1e-8) tolerance(1e-8)

{txt}initial:       log likelihood = {res}-4153.6604
{txt}rescale:       log likelihood = {res}-4153.6604
{txt}rescale eq:    log likelihood = {res}-4153.6604
{txt}Iteration 0:{col 16}log likelihood = {res}-4153.6604{txt}  
Iteration 1:{col 16}log likelihood = {res}-4148.6411{txt}  (backed up)
Iteration 2:{col 16}log likelihood = {res}-4146.4017{txt}  (backed up)
Iteration 3:{col 16}log likelihood = {res}-4146.4006{txt}  (backed up)
Iteration 4:{col 16}log likelihood = {res}-4145.9844{txt}  (backed up)
Iteration 5:{col 16}log likelihood = {res}-4145.4303{txt}  (backed up)
Iteration 6:{col 16}log likelihood = {res}-4145.1999{txt}  (backed up)
Iteration 7:{col 16}log likelihood = {res}-4145.0833{txt}  (backed up)
Iteration 8:{col 16}log likelihood = {res}-4144.8531{txt}  (backed up)
Iteration 9:{col 16}log likelihood = {res}-4144.6216{txt}  (backed up)
Iteration 10:{col 16}log likelihood = {res}-4144.1181{txt}  (backed up)
Iteration 11:{col 16}log likelihood = {res}-4143.2054{txt}  (backed up)
Iteration 12:{col 16}log likelihood = {res}-4142.9969{txt}  (backed up)
Iteration 13:{col 16}log likelihood = {res} -4141.734{txt}  (backed up)
Iteration 14:{col 16}log likelihood = {res}-4141.5889{txt}  (backed up)
Iteration 15:{col 16}log likelihood = {res}-4139.2685{txt}  (backed up)
Iteration 16:{col 16}log likelihood = {res}-4139.1001{txt}  (backed up)
Iteration 17:{col 16}log likelihood = {res}-4138.9323{txt}  (backed up)
Iteration 18:{col 16}log likelihood = {res} -4138.864{txt}  
Iteration 19:{col 16}log likelihood = {res}-4138.6338{txt}  (backed up)
Iteration 20:{col 16}log likelihood = {res}-4138.5386{txt}  
Iteration 21:{col 16}log likelihood = {res}-4138.3283{txt}  
Iteration 22:{col 16}log likelihood = {res}-4137.8329{txt}  
Iteration 23:{col 16}log likelihood = {res}-4137.2267{txt}  
Iteration 24:{col 16}log likelihood = {res}-4136.8335{txt}  
Iteration 25:{col 16}log likelihood = {res}-4136.8276{txt}  
Iteration 26:{col 16}log likelihood = {res}-4135.5539{txt}  
Iteration 27:{col 16}log likelihood = {res}-4135.1083{txt}  
Iteration 28:{col 16}log likelihood = {res}-4134.6358{txt}  
Iteration 29:{col 16}log likelihood = {res} -4134.353{txt}  
Iteration 30:{col 16}log likelihood = {res}-4134.0043{txt}  
Iteration 31:{col 16}log likelihood = {res}-4133.8122{txt}  
Iteration 32:{col 16}log likelihood = {res}-4133.7342{txt}  
Iteration 33:{col 16}log likelihood = {res}-4133.5596{txt}  
Iteration 34:{col 16}log likelihood = {res}-4133.5054{txt}  
Iteration 35:{col 16}log likelihood = {res}-4133.4353{txt}  
Iteration 36:{col 16}log likelihood = {res}-4133.4149{txt}  
Iteration 37:{col 16}log likelihood = {res}-4133.3742{txt}  
Iteration 38:{col 16}log likelihood = {res}-4133.3513{txt}  
Iteration 39:{col 16}log likelihood = {res}-4133.2861{txt}  
Iteration 40:{col 16}log likelihood = {res}-4133.2693{txt}  
Iteration 41:{col 16}log likelihood = {res}-4133.2185{txt}  
Iteration 42:{col 16}log likelihood = {res}-4133.2138{txt}  
Iteration 43:{col 16}log likelihood = {res}-4133.1958{txt}  
Iteration 44:{col 16}log likelihood = {res}-4133.1941{txt}  
Iteration 45:{col 16}log likelihood = {res}-4133.1882{txt}  
Iteration 46:{col 16}log likelihood = {res}-4133.1821{txt}  
Iteration 47:{col 16}log likelihood = {res}-4133.1684{txt}  
Iteration 48:{col 16}log likelihood = {res} -4133.162{txt}  
Iteration 49:{col 16}log likelihood = {res}-4133.1587{txt}  
Iteration 50:{col 16}log likelihood = {res}-4133.1566{txt}  
Iteration 51:{col 16}log likelihood = {res}-4133.1552{txt}  
Iteration 52:{col 16}log likelihood = {res}-4133.1535{txt}  
Iteration 53:{col 16}log likelihood = {res}-4133.1531{txt}  
Iteration 54:{col 16}log likelihood = {res}-4133.1511{txt}  
Iteration 55:{col 16}log likelihood = {res}-4133.1508{txt}  
Iteration 56:{col 16}log likelihood = {res}-4133.1497{txt}  
Iteration 57:{col 16}log likelihood = {res}-4133.1491{txt}  
Iteration 58:{col 16}log likelihood = {res}-4133.1482{txt}  
Iteration 59:{col 16}log likelihood = {res}-4133.1475{txt}  
Iteration 60:{col 16}log likelihood = {res} -4133.147{txt}  
Iteration 61:{col 16}log likelihood = {res}-4133.1463{txt}  
Iteration 62:{col 16}log likelihood = {res}-4133.1461{txt}  
Iteration 63:{col 16}log likelihood = {res}-4133.1455{txt}  
Iteration 64:{col 16}log likelihood = {res}-4133.1454{txt}  
Iteration 65:{col 16}log likelihood = {res} -4133.145{txt}  
Iteration 66:{col 16}log likelihood = {res}-4133.1449{txt}  
Iteration 67:{col 16}log likelihood = {res}-4133.1448{txt}  
Iteration 68:{col 16}log likelihood = {res}-4133.1447{txt}  
Iteration 69:{col 16}log likelihood = {res} -4133.144{txt}  
Iteration 70:{col 16}log likelihood = {res}-4133.1438{txt}  
Iteration 71:{col 16}log likelihood = {res}-4133.1433{txt}  
Iteration 72:{col 16}log likelihood = {res}-4133.1432{txt}  
Iteration 73:{col 16}log likelihood = {res} -4133.143{txt}  
Iteration 74:{col 16}log likelihood = {res}-4133.1428{txt}  
Iteration 75:{col 16}log likelihood = {res}-4133.1428{txt}  
Iteration 76:{col 16}log likelihood = {res}-4133.1425{txt}  
Iteration 77:{col 16}log likelihood = {res}-4133.1422{txt}  
Iteration 78:{col 16}log likelihood = {res}-4133.1421{txt}  
Iteration 79:{col 16}log likelihood = {res}-4133.1416{txt}  
Iteration 80:{col 16}log likelihood = {res}-4133.1412{txt}  
Iteration 81:{col 16}log likelihood = {res}-4133.1411{txt}  
Iteration 82:{col 16}log likelihood = {res} -4133.141{txt}  
Iteration 83:{col 16}log likelihood = {res}-4133.1409{txt}  
Iteration 84:{col 16}log likelihood = {res}-4133.1404{txt}  
Iteration 85:{col 16}log likelihood = {res}-4133.1398{txt}  
Iteration 86:{col 16}log likelihood = {res}-4133.1397{txt}  
Iteration 87:{col 16}log likelihood = {res}-4133.1396{txt}  
Iteration 88:{col 16}log likelihood = {res}-4133.1394{txt}  
Iteration 89:{col 16}log likelihood = {res}-4133.1391{txt}  
Iteration 90:{col 16}log likelihood = {res}-4133.1389{txt}  
Iteration 91:{col 16}log likelihood = {res}-4133.1387{txt}  
Iteration 92:{col 16}log likelihood = {res}-4133.1386{txt}  
Iteration 93:{col 16}log likelihood = {res}-4133.1386{txt}  
Iteration 94:{col 16}log likelihood = {res}-4133.1385{txt}  
Iteration 95:{col 16}log likelihood = {res}-4133.1383{txt}  
Iteration 96:{col 16}log likelihood = {res}-4133.1383{txt}  
Iteration 97:{col 16}log likelihood = {res}-4133.1382{txt}  
Iteration 98:{col 16}log likelihood = {res}-4133.1382{txt}  
Iteration 99:{col 16}log likelihood = {res}-4133.1382{txt}  
Iteration 100:{col 16}log likelihood = {res}-4133.1382{txt}  
Iteration 101:{col 16}log likelihood = {res}-4133.1381{txt}  
Iteration 102:{col 16}log likelihood = {res}-4133.1381{txt}  
Iteration 103:{col 16}log likelihood = {res}-4133.1381{txt}  
Iteration 104:{col 16}log likelihood = {res}-4133.1381{txt}  
Iteration 105:{col 16}log likelihood = {res}-4133.1381{txt}  
Iteration 106:{col 16}log likelihood = {res}-4133.1381{txt}  
Iteration 107:{col 16}log likelihood = {res}-4133.1381{txt}  
Iteration 108:{col 16}log likelihood = {res}-4133.1381{txt}  
Iteration 109:{col 16}log likelihood = {res}-4133.1381{txt}  
Iteration 110:{col 16}log likelihood = {res}-4133.1381{txt}  

{col 51}Number of obs{col 67}= {res}      3919
{txt}{col 51}Wald chi2({res}188{txt}){col 67}= {res}  12392.90
{txt}Log likelihood = {res}-4133.1381{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        ln_FGTD{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}mu              {txt}{c |}
{space 8}geddes1 {c |}{col 17}{res}{space 2}  -.13247{col 29}{space 2} .0693739{col 40}{space 1}   -1.91{col 49}{space 3}0.056{col 57}{space 4}-.2684403{col 70}{space 3} .0035003
{txt}{space 8}geddes2 {c |}{col 17}{res}{space 2} .0053341{col 29}{space 2} .0590956{col 40}{space 1}    0.09{col 49}{space 3}0.928{col 57}{space 4}-.1104911{col 70}{space 3} .1211592
{txt}{space 8}geddes3 {c |}{col 17}{res}{space 2}-.0344568{col 29}{space 2} .0749496{col 40}{space 1}   -0.46{col 49}{space 3}0.646{col 57}{space 4}-.1813552{col 70}{space 3} .1124417
{txt}{space 8}geddes4 {c |}{col 17}{res}{space 2}-.0983931{col 29}{space 2} .1832205{col 40}{space 1}   -0.54{col 49}{space 3}0.591{col 57}{space 4}-.4574986{col 70}{space 3} .2607125
{txt}{space 8}geddes5 {c |}{col 17}{res}{space 2} -.266243{col 29}{space 2} .1776441{col 40}{space 1}   -1.50{col 49}{space 3}0.134{col 57}{space 4}-.6144189{col 70}{space 3}  .081933
{txt}{space 9}logGNI {c |}{col 17}{res}{space 2}-.0949365{col 29}{space 2} .0364934{col 40}{space 1}   -2.60{col 49}{space 3}0.009{col 57}{space 4}-.1664623{col 70}{space 3}-.0234108
{txt}{space 9}logpop {c |}{col 17}{res}{space 2} .1804107{col 29}{space 2} .0873349{col 40}{space 1}    2.07{col 49}{space 3}0.039{col 57}{space 4} .0092374{col 70}{space 3}  .351584
{txt}{space 8}logarea {c |}{col 17}{res}{space 2}  .097727{col 29}{space 2} .0542482{col 40}{space 1}    1.80{col 49}{space 3}0.072{col 57}{space 4}-.0085976{col 70}{space 3} .2040516
{txt}{space 11}GINI {c |}{col 17}{res}{space 2} .0027729{col 29}{space 2} .0040773{col 40}{space 1}    0.68{col 49}{space 3}0.496{col 57}{space 4}-.0052186{col 70}{space 3} .0107643
{txt}{space 8}Durable {c |}{col 17}{res}{space 2}-.0018092{col 29}{space 2} .0014648{col 40}{space 1}   -1.24{col 49}{space 3}0.217{col 57}{space 4}-.0046801{col 70}{space 3} .0010617
{txt}{space 10}AggSF {c |}{col 17}{res}{space 2} .0272194{col 29}{space 2}   .01174{col 40}{space 1}    2.32{col 49}{space 3}0.020{col 57}{space 4} .0042093{col 70}{space 3} .0502294
{txt}{space 8}ColdWar {c |}{col 17}{res}{space 2}  -.27989{col 29}{space 2} .1205566{col 40}{space 1}   -2.32{col 49}{space 3}0.020{col 57}{space 4}-.5161767{col 70}{space 3}-.0436034
{txt}{space 5}ucdp_type2 {c |}{col 17}{res}{space 2}-.0160359{col 29}{space 2} .0256707{col 40}{space 1}   -0.62{col 49}{space 3}0.532{col 57}{space 4}-.0663495{col 70}{space 3} .0342778
{txt}{space 5}ucdp_type3 {c |}{col 17}{res}{space 2} .1707977{col 29}{space 2} .0246669{col 40}{space 1}    6.92{col 49}{space 3}0.000{col 57}{space 4} .1224516{col 70}{space 3} .2191439
{txt}log_iMigrantsBA {c |}{col 17}{res}{space 2}-.0642745{col 29}{space 2} .0291091{col 40}{space 1}   -2.21{col 49}{space 3}0.027{col 57}{space 4}-.1213273{col 70}{space 3}-.0072218
{txt}{space 11}LDV1 {c |}{col 17}{res}{space 2} .5179959{col 29}{space 2} .0138199{col 40}{space 1}   37.48{col 49}{space 3}0.000{col 57}{space 4} .4909095{col 70}{space 3} .5450824
{txt}{space 4}country_fe2 {c |}{col 17}{res}{space 2}-1.102378{col 29}{space 2} .2940615{col 40}{space 1}   -3.75{col 49}{space 3}0.000{col 57}{space 4}-1.678728{col 70}{space 3}-.5260281
{txt}{space 4}country_fe3 {c |}{col 17}{res}{space 2}-.8965175{col 29}{space 2} .2632813{col 40}{space 1}   -3.41{col 49}{space 3}0.001{col 57}{space 4}-1.412539{col 70}{space 3}-.3804955
{txt}{space 4}country_fe4 {c |}{col 17}{res}{space 2}-.6428039{col 29}{space 2}  .275901{col 40}{space 1}   -2.33{col 49}{space 3}0.020{col 57}{space 4} -1.18356{col 70}{space 3}-.1020478
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{txt}{space 2}country_fe102 {c |}{col 17}{res}{space 2}-.2729164{col 29}{space 2} .4305115{col 40}{space 1}   -0.63{col 49}{space 3}0.526{col 57}{space 4}-1.116703{col 70}{space 3} .5708706
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{txt}{space 2}country_fe107 {c |}{col 17}{res}{space 2}-.8533056{col 29}{space 2} .4111145{col 40}{space 1}   -2.08{col 49}{space 3}0.038{col 57}{space 4}-1.659075{col 70}{space 3} -.047536
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{txt}{space 2}country_fe110 {c |}{col 17}{res}{space 2}-.1823183{col 29}{space 2} .2518407{col 40}{space 1}   -0.72{col 49}{space 3}0.469{col 57}{space 4}-.6759171{col 70}{space 3} .3112805
{txt}{space 2}country_fe111 {c |}{col 17}{res}{space 2}-1.151837{col 29}{space 2} .3036459{col 40}{space 1}   -3.79{col 49}{space 3}0.000{col 57}{space 4}-1.746973{col 70}{space 3}-.5567024
{txt}{space 2}country_fe112 {c |}{col 17}{res}{space 2}-.8650896{col 29}{space 2} .3286316{col 40}{space 1}   -2.63{col 49}{space 3}0.008{col 57}{space 4}-1.509196{col 70}{space 3}-.2209836
{txt}{space 2}country_fe113 {c |}{col 17}{res}{space 2} -1.20043{col 29}{space 2} .3354767{col 40}{space 1}   -3.58{col 49}{space 3}0.000{col 57}{space 4}-1.857952{col 70}{space 3}-.5429074
{txt}{space 2}country_fe114 {c |}{col 17}{res}{space 2} .5752887{col 29}{space 2} .2581804{col 40}{space 1}    2.23{col 49}{space 3}0.026{col 57}{space 4} .0692645{col 70}{space 3} 1.081313
{txt}{space 2}country_fe115 {c |}{col 17}{res}{space 2} -.514265{col 29}{space 2}  .357059{col 40}{space 1}   -1.44{col 49}{space 3}0.150{col 57}{space 4}-1.214088{col 70}{space 3} .1855577
{txt}{space 2}country_fe116 {c |}{col 17}{res}{space 2} .7767275{col 29}{space 2}  .202975{col 40}{space 1}    3.83{col 49}{space 3}0.000{col 57}{space 4} .3789038{col 70}{space 3} 1.174551
{txt}{space 2}country_fe117 {c |}{col 17}{res}{space 2}-.7732662{col 29}{space 2}  .358087{col 40}{space 1}   -2.16{col 49}{space 3}0.031{col 57}{space 4}-1.475104{col 70}{space 3}-.0714286
{txt}{space 2}country_fe118 {c |}{col 17}{res}{space 2}  .178593{col 29}{space 2} .3209163{col 40}{space 1}    0.56{col 49}{space 3}0.578{col 57}{space 4}-.4503913{col 70}{space 3} .8075774
{txt}{space 2}country_fe119 {c |}{col 17}{res}{space 2}  .145071{col 29}{space 2} .3942638{col 40}{space 1}    0.37{col 49}{space 3}0.713{col 57}{space 4}-.6276719{col 70}{space 3} .9178139
{txt}{space 2}country_fe120 {c |}{col 17}{res}{space 2}-.5518683{col 29}{space 2} .4074951{col 40}{space 1}   -1.35{col 49}{space 3}0.176{col 57}{space 4}-1.350544{col 70}{space 3} .2468073
{txt}{space 2}country_fe121 {c |}{col 17}{res}{space 2}-1.556873{col 29}{space 2} .3559602{col 40}{space 1}   -4.37{col 49}{space 3}0.000{col 57}{space 4}-2.254542{col 70}{space 3}-.8592038
{txt}{space 2}country_fe122 {c |}{col 17}{res}{space 2}-1.408201{col 29}{space 2} .4418386{col 40}{space 1}   -3.19{col 49}{space 3}0.001{col 57}{space 4}-2.274189{col 70}{space 3}-.5422135
{txt}{space 2}country_fe123 {c |}{col 17}{res}{space 2}-.8586591{col 29}{space 2} .4030234{col 40}{space 1}   -2.13{col 49}{space 3}0.033{col 57}{space 4} -1.64857{col 70}{space 3}-.0687477
{txt}{space 2}country_fe124 {c |}{col 17}{res}{space 2}-2.426774{col 29}{space 2} .3593881{col 40}{space 1}   -6.75{col 49}{space 3}0.000{col 57}{space 4}-3.131162{col 70}{space 3}-1.722387
{txt}{space 2}country_fe125 {c |}{col 17}{res}{space 2}-1.363713{col 29}{space 2} .4900413{col 40}{space 1}   -2.78{col 49}{space 3}0.005{col 57}{space 4}-2.324177{col 70}{space 3}-.4032499
{txt}{space 2}country_fe126 {c |}{col 17}{res}{space 2}-1.280907{col 29}{space 2} .2559498{col 40}{space 1}   -5.00{col 49}{space 3}0.000{col 57}{space 4}-1.782559{col 70}{space 3}-.7792546
{txt}{space 2}country_fe127 {c |}{col 17}{res}{space 2}-1.178054{col 29}{space 2} .2151454{col 40}{space 1}   -5.48{col 49}{space 3}0.000{col 57}{space 4}-1.599731{col 70}{space 3}-.7563764
{txt}{space 2}country_fe128 {c |}{col 17}{res}{space 2}-.3194486{col 29}{space 2} .2219002{col 40}{space 1}   -1.44{col 49}{space 3}0.150{col 57}{space 4} -.754365{col 70}{space 3} .1154678
{txt}{space 2}country_fe129 {c |}{col 17}{res}{space 2}-.8496234{col 29}{space 2} .2849186{col 40}{space 1}   -2.98{col 49}{space 3}0.003{col 57}{space 4}-1.408054{col 70}{space 3}-.2911931
{txt}{space 2}country_fe130 {c |}{col 17}{res}{space 2}-.1107108{col 29}{space 2} .2715725{col 40}{space 1}   -0.41{col 49}{space 3}0.684{col 57}{space 4}-.6429831{col 70}{space 3} .4215615
{txt}{space 2}country_fe131 {c |}{col 17}{res}{space 2} -1.53881{col 29}{space 2} .3101255{col 40}{space 1}   -4.96{col 49}{space 3}0.000{col 57}{space 4}-2.146645{col 70}{space 3}-.9309755
{txt}{space 2}country_fe132 {c |}{col 17}{res}{space 2}-.8718304{col 29}{space 2} .2287633{col 40}{space 1}   -3.81{col 49}{space 3}0.000{col 57}{space 4}-1.320198{col 70}{space 3}-.4234626
{txt}{space 2}country_fe133 {c |}{col 17}{res}{space 2}-1.126355{col 29}{space 2} .2856711{col 40}{space 1}   -3.94{col 49}{space 3}0.000{col 57}{space 4} -1.68626{col 70}{space 3}-.5664496
{txt}{space 2}country_fe134 {c |}{col 17}{res}{space 2}-.8543758{col 29}{space 2} .2523056{col 40}{space 1}   -3.39{col 49}{space 3}0.001{col 57}{space 4}-1.348886{col 70}{space 3}-.3598659
{txt}{space 2}country_fe135 {c |}{col 17}{res}{space 2}-1.375859{col 29}{space 2} .3325109{col 40}{space 1}   -4.14{col 49}{space 3}0.000{col 57}{space 4}-2.027568{col 70}{space 3}-.7241495
{txt}{space 2}country_fe136 {c |}{col 17}{res}{space 2}-1.496476{col 29}{space 2} .3871492{col 40}{space 1}   -3.87{col 49}{space 3}0.000{col 57}{space 4}-2.255274{col 70}{space 3}-.7376771
{txt}{space 2}country_fe137 {c |}{col 17}{res}{space 2}-.9700205{col 29}{space 2} .2689209{col 40}{space 1}   -3.61{col 49}{space 3}0.000{col 57}{space 4}-1.497096{col 70}{space 3}-.4429453
{txt}{space 2}country_fe139 {c |}{col 17}{res}{space 2}-.3121365{col 29}{space 2} .2551122{col 40}{space 1}   -1.22{col 49}{space 3}0.221{col 57}{space 4}-.8121472{col 70}{space 3} .1878742
{txt}{space 2}country_fe140 {c |}{col 17}{res}{space 2}-1.345716{col 29}{space 2} .3310005{col 40}{space 1}   -4.07{col 49}{space 3}0.000{col 57}{space 4}-1.994465{col 70}{space 3}-.6969665
{txt}{space 2}country_fe141 {c |}{col 17}{res}{space 2}-.6063858{col 29}{space 2} .3231287{col 40}{space 1}   -1.88{col 49}{space 3}0.061{col 57}{space 4}-1.239706{col 70}{space 3} .0269348
{txt}{space 2}country_fe142 {c |}{col 17}{res}{space 2}-1.094992{col 29}{space 2} .3795936{col 40}{space 1}   -2.88{col 49}{space 3}0.004{col 57}{space 4}-1.838981{col 70}{space 3}-.3510019
{txt}{space 2}country_fe143 {c |}{col 17}{res}{space 2}-.5122213{col 29}{space 2} .2839996{col 40}{space 1}   -1.80{col 49}{space 3}0.071{col 57}{space 4} -1.06885{col 70}{space 3} .0444077
{txt}{space 2}country_fe144 {c |}{col 17}{res}{space 2}-.7042107{col 29}{space 2}  .449109{col 40}{space 1}   -1.57{col 49}{space 3}0.117{col 57}{space 4}-1.584448{col 70}{space 3} .1760268
{txt}{space 2}country_fe145 {c |}{col 17}{res}{space 2}-.4876812{col 29}{space 2}  .415357{col 40}{space 1}   -1.17{col 49}{space 3}0.240{col 57}{space 4}-1.301766{col 70}{space 3} .3264036
{txt}{space 7}year_fe2 {c |}{col 17}{res}{space 2}-.0010866{col 29}{space 2} .0921485{col 40}{space 1}   -0.01{col 49}{space 3}0.991{col 57}{space 4}-.1816942{col 70}{space 3} .1795211
{txt}{space 7}year_fe3 {c |}{col 17}{res}{space 2} .1422455{col 29}{space 2}  .091873{col 40}{space 1}    1.55{col 49}{space 3}0.122{col 57}{space 4}-.0378223{col 70}{space 3} .3223132
{txt}{space 7}year_fe4 {c |}{col 17}{res}{space 2} .1291971{col 29}{space 2} .0925782{col 40}{space 1}    1.40{col 49}{space 3}0.163{col 57}{space 4}-.0522528{col 70}{space 3} .3106469
{txt}{space 7}year_fe5 {c |}{col 17}{res}{space 2}  .181436{col 29}{space 2} .0940259{col 40}{space 1}    1.93{col 49}{space 3}0.054{col 57}{space 4}-.0028513{col 70}{space 3} .3657234
{txt}{space 7}year_fe6 {c |}{col 17}{res}{space 2} .2522033{col 29}{space 2} .0949551{col 40}{space 1}    2.66{col 49}{space 3}0.008{col 57}{space 4} .0660947{col 70}{space 3} .4383119
{txt}{space 7}year_fe7 {c |}{col 17}{res}{space 2}  .230103{col 29}{space 2} .0959078{col 40}{space 1}    2.40{col 49}{space 3}0.016{col 57}{space 4} .0421273{col 70}{space 3} .4180788
{txt}{space 7}year_fe8 {c |}{col 17}{res}{space 2} .4002688{col 29}{space 2} .0972997{col 40}{space 1}    4.11{col 49}{space 3}0.000{col 57}{space 4} .2095649{col 70}{space 3} .5909727
{txt}{space 7}year_fe9 {c |}{col 17}{res}{space 2} .5010886{col 29}{space 2}  .099179{col 40}{space 1}    5.05{col 49}{space 3}0.000{col 57}{space 4} .3067014{col 70}{space 3} .6954759
{txt}{space 6}year_fe10 {c |}{col 17}{res}{space 2} .3752764{col 29}{space 2} .1014144{col 40}{space 1}    3.70{col 49}{space 3}0.000{col 57}{space 4} .1765079{col 70}{space 3} .5740449
{txt}{space 6}year_fe11 {c |}{col 17}{res}{space 2} .3697658{col 29}{space 2} .1040688{col 40}{space 1}    3.55{col 49}{space 3}0.000{col 57}{space 4} .1657947{col 70}{space 3} .5737369
{txt}{space 6}year_fe12 {c |}{col 17}{res}{space 2} .2388299{col 29}{space 2}  .104115{col 40}{space 1}    2.29{col 49}{space 3}0.022{col 57}{space 4} .0347683{col 70}{space 3} .4428915
{txt}{space 6}year_fe13 {c |}{col 17}{res}{space 2} .4321088{col 29}{space 2} .1033205{col 40}{space 1}    4.18{col 49}{space 3}0.000{col 57}{space 4} .2296044{col 70}{space 3} .6346132
{txt}{space 6}year_fe14 {c |}{col 17}{res}{space 2} .3493569{col 29}{space 2} .1035531{col 40}{space 1}    3.37{col 49}{space 3}0.001{col 57}{space 4} .1463967{col 70}{space 3} .5523172
{txt}{space 6}year_fe15 {c |}{col 17}{res}{space 2} .3290093{col 29}{space 2} .1037462{col 40}{space 1}    3.17{col 49}{space 3}0.002{col 57}{space 4} .1256704{col 70}{space 3} .5323482
{txt}{space 6}year_fe16 {c |}{col 17}{res}{space 2} .3704651{col 29}{space 2} .1038867{col 40}{space 1}    3.57{col 49}{space 3}0.000{col 57}{space 4} .1668509{col 70}{space 3} .5740794
{txt}{space 6}year_fe17 {c |}{col 17}{res}{space 2} .3745895{col 29}{space 2} .1056865{col 40}{space 1}    3.54{col 49}{space 3}0.000{col 57}{space 4} .1674476{col 70}{space 3} .5817313
{txt}{space 6}year_fe18 {c |}{col 17}{res}{space 2} .4797054{col 29}{space 2} .1072034{col 40}{space 1}    4.47{col 49}{space 3}0.000{col 57}{space 4} .2695905{col 70}{space 3} .6898203
{txt}{space 6}year_fe19 {c |}{col 17}{res}{space 2} .5538997{col 29}{space 2} .1093244{col 40}{space 1}    5.07{col 49}{space 3}0.000{col 57}{space 4} .3396279{col 70}{space 3} .7681716
{txt}{space 6}year_fe20 {c |}{col 17}{res}{space 2}  .410644{col 29}{space 2} .1103353{col 40}{space 1}    3.72{col 49}{space 3}0.000{col 57}{space 4} .1943907{col 70}{space 3} .6268973
{txt}{space 6}year_fe21 {c |}{col 17}{res}{space 2} .7680565{col 29}{space 2} .1121309{col 40}{space 1}    6.85{col 49}{space 3}0.000{col 57}{space 4} .5482841{col 70}{space 3} .9878289
{txt}{space 6}year_fe22 {c |}{col 17}{res}{space 2}    .6606{col 29}{space 2} .0885277{col 40}{space 1}    7.46{col 49}{space 3}0.000{col 57}{space 4} .4870888{col 70}{space 3} .8341111
{txt}{space 6}year_fe23 {c |}{col 17}{res}{space 2} 1.676311{col 29}{space 2}  .087825{col 40}{space 1}   19.09{col 49}{space 3}0.000{col 57}{space 4} 1.504177{col 70}{space 3} 1.848445
{txt}{space 6}year_fe24 {c |}{col 17}{res}{space 2}-.5317974{col 29}{space 2} .0927254{col 40}{space 1}   -5.74{col 49}{space 3}0.000{col 57}{space 4}-.7135359{col 70}{space 3}-.3500589
{txt}{space 6}year_fe25 {c |}{col 17}{res}{space 2} .0782002{col 29}{space 2} .0862381{col 40}{space 1}    0.91{col 49}{space 3}0.365{col 57}{space 4}-.0908234{col 70}{space 3} .2472237
{txt}{space 6}year_fe26 {c |}{col 17}{res}{space 2} .1660514{col 29}{space 2} .0855356{col 40}{space 1}    1.94{col 49}{space 3}0.052{col 57}{space 4}-.0015952{col 70}{space 3}  .333698
{txt}{space 6}year_fe27 {c |}{col 17}{res}{space 2} .2350847{col 29}{space 2} .0851473{col 40}{space 1}    2.76{col 49}{space 3}0.006{col 57}{space 4}  .068199{col 70}{space 3} .4019704
{txt}{space 6}year_fe28 {c |}{col 17}{res}{space 2}-.3705748{col 29}{space 2}  .084989{col 40}{space 1}   -4.36{col 49}{space 3}0.000{col 57}{space 4}-.5371502{col 70}{space 3}-.2039994
{txt}{space 6}year_fe29 {c |}{col 17}{res}{space 2} .0925193{col 29}{space 2}  .084188{col 40}{space 1}    1.10{col 49}{space 3}0.272{col 57}{space 4}-.0724863{col 70}{space 3} .2575248
{txt}{space 6}year_fe30 {c |}{col 17}{res}{space 2}-.0241949{col 29}{space 2} .0840966{col 40}{space 1}   -0.29{col 49}{space 3}0.774{col 57}{space 4}-.1890211{col 70}{space 3} .1406314
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .8397514{col 29}{space 2} .5727388{col 40}{space 1}    1.47{col 49}{space 3}0.143{col 57}{space 4}-.2827959{col 70}{space 3} 1.962299
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}rho             {txt}{c |}
{space 10}_cons {c |}{col 17}{res}{space 2} .0896217{col 29}{space 2} .0116638{col 40}{space 1}    7.68{col 49}{space 3}0.000{col 57}{space 4} .0667612{col 70}{space 3} .1124823
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}sigma           {txt}{c |}
{space 10}_cons {c |}{col 17}{res}{space 2} .6938669{col 29}{space 2} .0078404{col 40}{space 1}   88.50{col 49}{space 3}0.000{col 57}{space 4} .6785001{col 70}{space 3} .7092337
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *********************************
. * Model Estimation 2 - Distance *
. *********************************
. 
. sort year ccode
{txt}
{com}. 
. clear
{txt}
{com}. pr drop _all
{txt}
{com}. set matsize 8000
{txt}
{com}. set more off
{txt}
{com}. version 9
{txt}
{com}. 
. *  Likelihood Evaluator *                                                          
. 
. program define splag_ll
{txt}  1{com}. 
. args lnf mu rho sigma
{txt}  2{com}. tempvar A rSL
{txt}  3{com}. gen `A'= ones - `rho'*EIGS1
{txt}  4{com}. gen `rSL'=`rho'*SL1
{txt}  5{com}. qui replace `lnf'= ln(`A') + ln(normden($ML_y1-`rSL'-`mu', 0, `sigma'))
{txt}  6{com}. end
{txt}
{com}. 
. global nobs = 3919
{txt}
{com}. 
. * Open Data For Weights *
. 
. clear
{txt}
{com}. spatwmat using "Inv_Distance Matrix.dta", name(W) standardize


{txt}The following matrix has been created:

1. Imported non-binary weights matrix {res}W{txt} (row-standardized)
   Dimension: {res}3919x3919


{txt}
{com}. matrix eigenvalues eig1 imaginaryv = W
{txt}
{com}. matrix eig2 = eig1'
{txt}
{com}. matrix ones=J($nobs,1,1)
{txt}
{com}. 
. * Open Data for Regression *
. 
. drop _all
{txt}
{com}. 
. use "Data Set I.dta", clear
{txt}
{com}. 
. global Y ln_FGTD
{txt}
{com}. global X geddes1-geddes5 logGNI logpop logarea GINI Durable AggSF ColdWar ucdp_type2 ucdp_type3 log_iMigrantsBA LDV1 country_fe2-country_fe145 year_fe2-year_fe31
{txt}
{com}. global Z LDV1
{txt}
{com}. mkmat $Y, matrix(Y)
{res}{txt}
{com}. mkmat $Z, matrix(Z)
{res}{txt}
{com}. matrix SL = W*Z
{txt}
{com}. svmat SL, n(SL)
{txt}
{com}. svmat eig2, n(EIGS)
{txt}
{com}. svmat ones, n(ones)
{txt}
{com}. 
. * Produce Starting Values * 
. 
. qui regress $Y $X
{txt}
{com}. matrix OLSb=e(b)
{txt}
{com}. local OLSsigma=e(rmse)
{txt}
{com}. 
. * Estimate Spatial Lag Model *
. 
. ml model lf splag_ll (mu: $Y=$X) (rho:) (sigma:), technique(dfp) vce(oim)
{txt}note: country_fe138 dropped because of collinearity
note: year_fe31 dropped because of collinearity

{com}. ml init OLSb, skip
{txt}
{com}. ml init rho:_cons=0
{txt}
{com}. ml init sigma:_cons=`OLSsigma'
{txt}
{com}. ml max, difficult ltolerance(1e-8) tolerance(1e-8)

{txt}initial:       log likelihood = {res}-4153.6604
{txt}rescale:       log likelihood = {res}-4153.6604
{txt}rescale eq:    log likelihood = {res}-4153.6604
{txt}Iteration 0:{col 16}log likelihood = {res}-4153.6604{txt}  
Iteration 1:{col 16}log likelihood = {res}-4153.1076{txt}  (backed up)
Iteration 2:{col 16}log likelihood = {res}-4153.0966{txt}  (backed up)
Iteration 3:{col 16}log likelihood = {res}-4153.0963{txt}  (backed up)
Iteration 4:{col 16}log likelihood = {res}-4153.0782{txt}  (backed up)
Iteration 5:{col 16}log likelihood = {res}-4152.9869{txt}  (backed up)
Iteration 6:{col 16}log likelihood = {res}-4152.9848{txt}  (backed up)
Iteration 7:{col 16}log likelihood = {res}-4152.9518{txt}  (backed up)
Iteration 8:{col 16}log likelihood = {res}-4152.8911{txt}  (backed up)
Iteration 9:{col 16}log likelihood = {res} -4152.861{txt}  (backed up)
Iteration 10:{col 16}log likelihood = {res}-4152.4657{txt}  (backed up)
Iteration 11:{col 16}log likelihood = {res}-4152.2221{txt}  (backed up)
Iteration 12:{col 16}log likelihood = {res} -4151.712{txt}  (backed up)
Iteration 13:{col 16}log likelihood = {res}-4151.5979{txt}  (backed up)
Iteration 14:{col 16}log likelihood = {res}-4151.5399{txt}  (backed up)
Iteration 15:{col 16}log likelihood = {res}-4151.1974{txt}  
Iteration 16:{col 16}log likelihood = {res}-4150.9511{txt}  
Iteration 17:{col 16}log likelihood = {res}-4150.7609{txt}  (backed up)
Iteration 18:{col 16}log likelihood = {res}-4150.7017{txt}  
Iteration 19:{col 16}log likelihood = {res}-4150.3005{txt}  
Iteration 20:{col 16}log likelihood = {res}-4149.6913{txt}  
Iteration 21:{col 16}log likelihood = {res}-4149.5074{txt}  
Iteration 22:{col 16}log likelihood = {res}-4149.0964{txt}  
Iteration 23:{col 16}log likelihood = {res} -4148.755{txt}  
Iteration 24:{col 16}log likelihood = {res}-4148.7002{txt}  
Iteration 25:{col 16}log likelihood = {res}-4148.4947{txt}  
Iteration 26:{col 16}log likelihood = {res}-4148.1243{txt}  
Iteration 27:{col 16}log likelihood = {res}-4147.7631{txt}  
Iteration 28:{col 16}log likelihood = {res}-4147.6184{txt}  
Iteration 29:{col 16}log likelihood = {res}-4147.3798{txt}  
Iteration 30:{col 16}log likelihood = {res}-4147.1495{txt}  
Iteration 31:{col 16}log likelihood = {res}-4147.1323{txt}  
Iteration 32:{col 16}log likelihood = {res}-4146.9945{txt}  
Iteration 33:{col 16}log likelihood = {res}-4146.9329{txt}  
Iteration 34:{col 16}log likelihood = {res}-4146.8839{txt}  
Iteration 35:{col 16}log likelihood = {res}-4146.8732{txt}  
Iteration 36:{col 16}log likelihood = {res}-4146.7888{txt}  
Iteration 37:{col 16}log likelihood = {res}-4146.7544{txt}  
Iteration 38:{col 16}log likelihood = {res}-4146.6308{txt}  
Iteration 39:{col 16}log likelihood = {res}-4146.5673{txt}  
Iteration 40:{col 16}log likelihood = {res}-4146.4585{txt}  
Iteration 41:{col 16}log likelihood = {res}-4146.4423{txt}  
Iteration 42:{col 16}log likelihood = {res}-4146.4416{txt}  
Iteration 43:{col 16}log likelihood = {res}-4146.4337{txt}  
Iteration 44:{col 16}log likelihood = {res}-4146.4251{txt}  
Iteration 45:{col 16}log likelihood = {res}-4146.4144{txt}  
Iteration 46:{col 16}log likelihood = {res}-4146.4101{txt}  
Iteration 47:{col 16}log likelihood = {res}-4146.3948{txt}  
Iteration 48:{col 16}log likelihood = {res}-4146.3902{txt}  
Iteration 49:{col 16}log likelihood = {res}-4146.3837{txt}  
Iteration 50:{col 16}log likelihood = {res}-4146.3825{txt}  
Iteration 51:{col 16}log likelihood = {res}-4146.3823{txt}  
Iteration 52:{col 16}log likelihood = {res}-4146.3813{txt}  
Iteration 53:{col 16}log likelihood = {res}-4146.3768{txt}  
Iteration 54:{col 16}log likelihood = {res}-4146.3758{txt}  
Iteration 55:{col 16}log likelihood = {res}-4146.3741{txt}  
Iteration 56:{col 16}log likelihood = {res}-4146.3728{txt}  
Iteration 57:{col 16}log likelihood = {res}-4146.3724{txt}  
Iteration 58:{col 16}log likelihood = {res}-4146.3709{txt}  
Iteration 59:{col 16}log likelihood = {res}-4146.3704{txt}  
Iteration 60:{col 16}log likelihood = {res}-4146.3699{txt}  
Iteration 61:{col 16}log likelihood = {res}-4146.3698{txt}  
Iteration 62:{col 16}log likelihood = {res}-4146.3696{txt}  
Iteration 63:{col 16}log likelihood = {res}-4146.3696{txt}  
Iteration 64:{col 16}log likelihood = {res}-4146.3696{txt}  
Iteration 65:{col 16}log likelihood = {res}-4146.3695{txt}  
Iteration 66:{col 16}log likelihood = {res}-4146.3694{txt}  
Iteration 67:{col 16}log likelihood = {res}-4146.3692{txt}  
Iteration 68:{col 16}log likelihood = {res}-4146.3691{txt}  
Iteration 69:{col 16}log likelihood = {res}-4146.3691{txt}  
Iteration 70:{col 16}log likelihood = {res} -4146.369{txt}  
Iteration 71:{col 16}log likelihood = {res} -4146.369{txt}  
Iteration 72:{col 16}log likelihood = {res} -4146.369{txt}  
Iteration 73:{col 16}log likelihood = {res} -4146.369{txt}  
Iteration 74:{col 16}log likelihood = {res} -4146.369{txt}  
Iteration 75:{col 16}log likelihood = {res} -4146.369{txt}  
Iteration 76:{col 16}log likelihood = {res} -4146.369{txt}  
Iteration 77:{col 16}log likelihood = {res} -4146.369{txt}  
Iteration 78:{col 16}log likelihood = {res}-4146.3689{txt}  
Iteration 79:{col 16}log likelihood = {res}-4146.3689{txt}  
Iteration 80:{col 16}log likelihood = {res}-4146.3689{txt}  
Iteration 81:{col 16}log likelihood = {res}-4146.3689{txt}  
Iteration 82:{col 16}log likelihood = {res}-4146.3689{txt}  
Iteration 83:{col 16}log likelihood = {res}-4146.3689{txt}  
Iteration 84:{col 16}log likelihood = {res}-4146.3689{txt}  
Iteration 85:{col 16}log likelihood = {res}-4146.3689{txt}  
Iteration 86:{col 16}log likelihood = {res}-4146.3689{txt}  
Iteration 87:{col 16}log likelihood = {res}-4146.3689{txt}  
Iteration 88:{col 16}log likelihood = {res}-4146.3689{txt}  
Iteration 89:{col 16}log likelihood = {res}-4146.3689{txt}  
Iteration 90:{col 16}log likelihood = {res}-4146.3689{txt}  
Iteration 91:{col 16}log likelihood = {res}-4146.3689{txt}  
Iteration 92:{col 16}log likelihood = {res}-4146.3689{txt}  
Iteration 93:{col 16}log likelihood = {res}-4146.3689{txt}  (backed up)
Iteration 94:{col 16}log likelihood = {res}-4146.3689{txt}  
Iteration 95:{col 16}log likelihood = {res}-4146.3689{txt}  
Iteration 96:{col 16}log likelihood = {res}-4146.3689{txt}  
Iteration 97:{col 16}log likelihood = {res}-4146.3688{txt}  
Iteration 98:{col 16}log likelihood = {res}-4146.3688{txt}  (backed up)
Iteration 99:{col 16}log likelihood = {res}-4146.3688{txt}  (backed up)
Iteration 100:{col 16}log likelihood = {res}-4146.3688{txt}  
Iteration 101:{col 16}log likelihood = {res}-4146.3688{txt}  
Iteration 102:{col 16}log likelihood = {res}-4146.3688{txt}  (backed up)
Iteration 103:{col 16}log likelihood = {res}-4146.3688{txt}  (backed up)
Iteration 104:{col 16}log likelihood = {res}-4146.3688{txt}  
Iteration 105:{col 16}log likelihood = {res}-4146.3688{txt}  
Iteration 106:{col 16}log likelihood = {res}-4146.3688{txt}  
Iteration 107:{col 16}log likelihood = {res}-4146.3688{txt}  
Iteration 108:{col 16}log likelihood = {res}-4146.3688{txt}  
Iteration 109:{col 16}log likelihood = {res}-4146.3688{txt}  (backed up)
Iteration 110:{col 16}log likelihood = {res}-4146.3688{txt}  (backed up)
Iteration 111:{col 16}log likelihood = {res}-4146.3688{txt}  

{col 51}Number of obs{col 67}= {res}      3919
{txt}{col 51}Wald chi2({res}188{txt}){col 67}= {res}  12620.07
{txt}Log likelihood = {res}-4146.3688{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        ln_FGTD{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}mu              {txt}{c |}
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{txt}{space 7}year_fe7 {c |}{col 17}{res}{space 2} .1649665{col 29}{space 2} .0980102{col 40}{space 1}    1.68{col 49}{space 3}0.092{col 57}{space 4}  -.02713{col 70}{space 3}  .357063
{txt}{space 7}year_fe8 {c |}{col 17}{res}{space 2} .3220863{col 29}{space 2} .1000984{col 40}{space 1}    3.22{col 49}{space 3}0.001{col 57}{space 4}  .125897{col 70}{space 3} .5182757
{txt}{space 7}year_fe9 {c |}{col 17}{res}{space 2} .3786061{col 29}{space 2} .1047113{col 40}{space 1}    3.62{col 49}{space 3}0.000{col 57}{space 4} .1733758{col 70}{space 3} .5838364
{txt}{space 6}year_fe10 {c |}{col 17}{res}{space 2} .1912067{col 29}{space 2} .1118833{col 40}{space 1}    1.71{col 49}{space 3}0.087{col 57}{space 4}-.0280805{col 70}{space 3} .4104938
{txt}{space 6}year_fe11 {c |}{col 17}{res}{space 2}  .188914{col 29}{space 2} .1145833{col 40}{space 1}    1.65{col 49}{space 3}0.099{col 57}{space 4}-.0356652{col 70}{space 3} .4134932
{txt}{space 6}year_fe12 {c |}{col 17}{res}{space 2} .0552004{col 29}{space 2} .1145963{col 40}{space 1}    0.48{col 49}{space 3}0.630{col 57}{space 4}-.1694042{col 70}{space 3}  .279805
{txt}{space 6}year_fe13 {c |}{col 17}{res}{space 2} .2925521{col 29}{space 2} .1102256{col 40}{space 1}    2.65{col 49}{space 3}0.008{col 57}{space 4} .0765139{col 70}{space 3} .5085903
{txt}{space 6}year_fe14 {c |}{col 17}{res}{space 2} .1683309{col 29}{space 2} .1136718{col 40}{space 1}    1.48{col 49}{space 3}0.139{col 57}{space 4}-.0544618{col 70}{space 3} .3911235
{txt}{space 6}year_fe15 {c |}{col 17}{res}{space 2} .1535513{col 29}{space 2} .1134772{col 40}{space 1}    1.35{col 49}{space 3}0.176{col 57}{space 4}  -.06886{col 70}{space 3} .3759626
{txt}{space 6}year_fe16 {c |}{col 17}{res}{space 2} .1932536{col 29}{space 2} .1133393{col 40}{space 1}    1.71{col 49}{space 3}0.088{col 57}{space 4}-.0288872{col 70}{space 3} .4153945
{txt}{space 6}year_fe17 {c |}{col 17}{res}{space 2} .1911501{col 29}{space 2} .1156368{col 40}{space 1}    1.65{col 49}{space 3}0.098{col 57}{space 4}-.0354938{col 70}{space 3}  .417794
{txt}{space 6}year_fe18 {c |}{col 17}{res}{space 2} .2913372{col 29}{space 2} .1173567{col 40}{space 1}    2.48{col 49}{space 3}0.013{col 57}{space 4} .0613224{col 70}{space 3} .5213521
{txt}{space 6}year_fe19 {c |}{col 17}{res}{space 2}  .339782{col 29}{space 2} .1222224{col 40}{space 1}    2.78{col 49}{space 3}0.005{col 57}{space 4} .1002304{col 70}{space 3} .5793335
{txt}{space 6}year_fe20 {c |}{col 17}{res}{space 2} .1557229{col 29}{space 2}  .127825{col 40}{space 1}    1.22{col 49}{space 3}0.223{col 57}{space 4}-.0948094{col 70}{space 3} .4062553
{txt}{space 6}year_fe21 {c |}{col 17}{res}{space 2} .5346207{col 29}{space 2} .1270066{col 40}{space 1}    4.21{col 49}{space 3}0.000{col 57}{space 4} .2856924{col 70}{space 3} .7835491
{txt}{space 6}year_fe22 {c |}{col 17}{res}{space 2}  .488352{col 29}{space 2} .0978421{col 40}{space 1}    4.99{col 49}{space 3}0.000{col 57}{space 4}  .296585{col 70}{space 3}  .680119
{txt}{space 6}year_fe23 {c |}{col 17}{res}{space 2} 1.378426{col 29}{space 2} .1124375{col 40}{space 1}   12.26{col 49}{space 3}0.000{col 57}{space 4} 1.158053{col 70}{space 3}   1.5988
{txt}{space 6}year_fe24 {c |}{col 17}{res}{space 2}-1.216674{col 29}{space 2} .1795912{col 40}{space 1}   -6.77{col 49}{space 3}0.000{col 57}{space 4}-1.568666{col 70}{space 3}-.8646815
{txt}{space 6}year_fe25 {c |}{col 17}{res}{space 2}-.1599321{col 29}{space 2} .1028862{col 40}{space 1}   -1.55{col 49}{space 3}0.120{col 57}{space 4}-.3615853{col 70}{space 3}  .041721
{txt}{space 6}year_fe26 {c |}{col 17}{res}{space 2} .0016303{col 29}{space 2} .0939227{col 40}{space 1}    0.02{col 49}{space 3}0.986{col 57}{space 4}-.1824547{col 70}{space 3} .1857154
{txt}{space 6}year_fe27 {c |}{col 17}{res}{space 2} .0924083{col 29}{space 2} .0915587{col 40}{space 1}    1.01{col 49}{space 3}0.313{col 57}{space 4}-.0870434{col 70}{space 3}   .27186
{txt}{space 6}year_fe28 {c |}{col 17}{res}{space 2}-.5077766{col 29}{space 2} .0913952{col 40}{space 1}   -5.56{col 49}{space 3}0.000{col 57}{space 4}-.6869079{col 70}{space 3}-.3286454
{txt}{space 6}year_fe29 {c |}{col 17}{res}{space 2} .1015028{col 29}{space 2}  .084548{col 40}{space 1}    1.20{col 49}{space 3}0.230{col 57}{space 4}-.0642083{col 70}{space 3} .2672139
{txt}{space 6}year_fe30 {c |}{col 17}{res}{space 2}-.0458151{col 29}{space 2} .0845277{col 40}{space 1}   -0.54{col 49}{space 3}0.588{col 57}{space 4}-.2114864{col 70}{space 3} .1198561
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}  .699253{col 29}{space 2} .5766482{col 40}{space 1}    1.21{col 49}{space 3}0.225{col 57}{space 4}-.4309566{col 70}{space 3} 1.829463
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}rho             {txt}{c |}
{space 10}_cons {c |}{col 17}{res}{space 2} .3552225{col 29}{space 2} .0661479{col 40}{space 1}    5.37{col 49}{space 3}0.000{col 57}{space 4}  .225575{col 70}{space 3} .4848699
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}sigma           {txt}{c |}
{space 10}_cons {c |}{col 17}{res}{space 2} .6965366{col 29}{space 2} .0078705{col 40}{space 1}   88.50{col 49}{space 3}0.000{col 57}{space 4} .6811107{col 70}{space 3} .7119625
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. ******************************************
. * Model Estimation 3 - Migration Inflows *
. ******************************************
. 
. sort year ccode
{txt}
{com}. 
. clear
{txt}
{com}. pr drop _all
{txt}
{com}. set matsize 8000
{txt}
{com}. set more off
{txt}
{com}. version 9
{txt}
{com}. 
. *  Likelihood Evaluator *                                                          
. 
. program define splag_ll
{txt}  1{com}. 
. args lnf mu rho sigma
{txt}  2{com}. tempvar A rSL
{txt}  3{com}. gen `A'= ones - `rho'*EIGS1
{txt}  4{com}. gen `rSL'=`rho'*SL1
{txt}  5{com}. qui replace `lnf'= ln(`A') + ln(normden($ML_y1-`rSL'-`mu', 0, `sigma'))
{txt}  6{com}. end
{txt}
{com}. 
. global nobs = 3919
{txt}
{com}. 
. * Open Data For Weights *
. 
. clear
{txt}
{com}. spatwmat using "Matrix.dta", name(W) standardize


{txt}The following matrix has been created:

1. Imported non-binary weights matrix {res}W{txt} (row-standardized)
   Dimension: {res}3919x3919

{err}   Beware! 1 location has no neighbors


{txt}
{com}. matrix eigenvalues eig1 imaginaryv = W
{txt}
{com}. matrix eig2 = eig1'
{txt}
{com}. matrix ones=J($nobs,1,1)
{txt}
{com}. 
. * Open Data for Regression *
. 
. drop _all
{txt}
{com}. 
. use "Data Set I.dta", clear
{txt}
{com}. 
. global Y ln_FGTD
{txt}
{com}. global X geddes1-geddes5 logGNI logpop logarea GINI Durable AggSF ColdWar ucdp_type2 ucdp_type3 log_iMigrantsBA LDV1 country_fe2-country_fe145 year_fe2-year_fe31
{txt}
{com}. global Z LDV1
{txt}
{com}. mkmat $Y, matrix(Y)
{res}{txt}
{com}. mkmat $Z, matrix(Z)
{res}{txt}
{com}. matrix SL = W*Z
{txt}
{com}. svmat SL, n(SL)
{txt}
{com}. svmat eig2, n(EIGS)
{txt}
{com}. svmat ones, n(ones)
{txt}
{com}. 
. * Produce Starting Values * 
. 
. qui regress $Y $X
{txt}
{com}. matrix OLSb=e(b)
{txt}
{com}. local OLSsigma=e(rmse)
{txt}
{com}. 
. * Estimate Spatial Lag Model *
. 
. ml model lf splag_ll (mu: $Y=$X) (rho:) (sigma:), technique(dfp) vce(oim)
{txt}note: country_fe138 dropped because of collinearity
note: year_fe31 dropped because of collinearity

{com}. ml init OLSb, skip
{txt}
{com}. ml init rho:_cons=0
{txt}
{com}. ml init sigma:_cons=`OLSsigma'
{txt}
{com}. ml max, difficult ltolerance(1e-8) tolerance(1e-8)

{txt}initial:       log likelihood = {res}-4153.6604
{txt}rescale:       log likelihood = {res}-4153.6604
{txt}rescale eq:    log likelihood = {res}-4153.6604
{txt}Iteration 0:{col 16}log likelihood = {res}-4153.6604{txt}  
Iteration 1:{col 16}log likelihood = {res} -4151.122{txt}  (backed up)
Iteration 2:{col 16}log likelihood = {res}-4149.0319{txt}  (backed up)
Iteration 3:{col 16}log likelihood = {res}-4148.9898{txt}  (backed up)
Iteration 4:{col 16}log likelihood = {res} -4148.643{txt}  (backed up)
Iteration 5:{col 16}log likelihood = {res}-4148.2619{txt}  (backed up)
Iteration 6:{col 16}log likelihood = {res}-4148.0696{txt}  (backed up)
Iteration 7:{col 16}log likelihood = {res}-4147.9895{txt}  (backed up)
Iteration 8:{col 16}log likelihood = {res}-4147.7474{txt}  (backed up)
Iteration 9:{col 16}log likelihood = {res}-4147.6414{txt}  (backed up)
Iteration 10:{col 16}log likelihood = {res}-4147.0728{txt}  (backed up)
Iteration 11:{col 16}log likelihood = {res}-4146.7116{txt}  (backed up)
Iteration 12:{col 16}log likelihood = {res}-4146.0493{txt}  (backed up)
Iteration 13:{col 16}log likelihood = {res}-4145.3409{txt}  (backed up)
Iteration 14:{col 16}log likelihood = {res}-4144.6418{txt}  (backed up)
Iteration 15:{col 16}log likelihood = {res}-4143.3425{txt}  (backed up)
Iteration 16:{col 16}log likelihood = {res}-4143.2239{txt}  (backed up)
Iteration 17:{col 16}log likelihood = {res}-4143.0803{txt}  (backed up)
Iteration 18:{col 16}log likelihood = {res}-4143.0044{txt}  (backed up)
Iteration 19:{col 16}log likelihood = {res}-4142.8603{txt}  
Iteration 20:{col 16}log likelihood = {res}-4142.5694{txt}  
Iteration 21:{col 16}log likelihood = {res}-4142.3922{txt}  
Iteration 22:{col 16}log likelihood = {res}-4142.1322{txt}  
Iteration 23:{col 16}log likelihood = {res}-4142.0946{txt}  
Iteration 24:{col 16}log likelihood = {res} -4141.701{txt}  
Iteration 25:{col 16}log likelihood = {res}-4141.4348{txt}  
Iteration 26:{col 16}log likelihood = {res}-4141.4239{txt}  
Iteration 27:{col 16}log likelihood = {res}-4141.3404{txt}  
Iteration 28:{col 16}log likelihood = {res}-4141.1147{txt}  
Iteration 29:{col 16}log likelihood = {res}-4141.0469{txt}  
Iteration 30:{col 16}log likelihood = {res}-4140.8362{txt}  
Iteration 31:{col 16}log likelihood = {res}-4140.6591{txt}  
Iteration 32:{col 16}log likelihood = {res}-4140.5635{txt}  
Iteration 33:{col 16}log likelihood = {res}-4140.4568{txt}  
Iteration 34:{col 16}log likelihood = {res} -4140.409{txt}  
Iteration 35:{col 16}log likelihood = {res}-4140.4074{txt}  
Iteration 36:{col 16}log likelihood = {res}-4140.3846{txt}  
Iteration 37:{col 16}log likelihood = {res}-4140.3377{txt}  
Iteration 38:{col 16}log likelihood = {res}-4140.3299{txt}  
Iteration 39:{col 16}log likelihood = {res}-4140.3189{txt}  
Iteration 40:{col 16}log likelihood = {res}-4140.3109{txt}  
Iteration 41:{col 16}log likelihood = {res}-4140.2787{txt}  
Iteration 42:{col 16}log likelihood = {res}-4140.2764{txt}  
Iteration 43:{col 16}log likelihood = {res}-4140.2557{txt}  
Iteration 44:{col 16}log likelihood = {res}  -4140.24{txt}  
Iteration 45:{col 16}log likelihood = {res}-4140.2156{txt}  
Iteration 46:{col 16}log likelihood = {res}-4140.2089{txt}  
Iteration 47:{col 16}log likelihood = {res}-4140.2025{txt}  
Iteration 48:{col 16}log likelihood = {res}-4140.1988{txt}  
Iteration 49:{col 16}log likelihood = {res}-4140.1961{txt}  
Iteration 50:{col 16}log likelihood = {res}-4140.1923{txt}  
Iteration 51:{col 16}log likelihood = {res}-4140.1907{txt}  
Iteration 52:{col 16}log likelihood = {res}-4140.1872{txt}  
Iteration 53:{col 16}log likelihood = {res}-4140.1859{txt}  
Iteration 54:{col 16}log likelihood = {res} -4140.183{txt}  
Iteration 55:{col 16}log likelihood = {res}-4140.1822{txt}  
Iteration 56:{col 16}log likelihood = {res}  -4140.18{txt}  
Iteration 57:{col 16}log likelihood = {res}-4140.1799{txt}  
Iteration 58:{col 16}log likelihood = {res}-4140.1789{txt}  
Iteration 59:{col 16}log likelihood = {res}-4140.1772{txt}  
Iteration 60:{col 16}log likelihood = {res}-4140.1762{txt}  
Iteration 61:{col 16}log likelihood = {res}-4140.1753{txt}  
Iteration 62:{col 16}log likelihood = {res}-4140.1751{txt}  
Iteration 63:{col 16}log likelihood = {res}-4140.1739{txt}  
Iteration 64:{col 16}log likelihood = {res}-4140.1738{txt}  
Iteration 65:{col 16}log likelihood = {res}-4140.1732{txt}  
Iteration 66:{col 16}log likelihood = {res}-4140.1726{txt}  
Iteration 67:{col 16}log likelihood = {res}-4140.1725{txt}  
Iteration 68:{col 16}log likelihood = {res}-4140.1718{txt}  
Iteration 69:{col 16}log likelihood = {res}-4140.1713{txt}  
Iteration 70:{col 16}log likelihood = {res}-4140.1697{txt}  
Iteration 71:{col 16}log likelihood = {res}-4140.1697{txt}  
Iteration 72:{col 16}log likelihood = {res}-4140.1688{txt}  
Iteration 73:{col 16}log likelihood = {res}-4140.1686{txt}  
Iteration 74:{col 16}log likelihood = {res}-4140.1677{txt}  
Iteration 75:{col 16}log likelihood = {res}-4140.1676{txt}  
Iteration 76:{col 16}log likelihood = {res}-4140.1671{txt}  
Iteration 77:{col 16}log likelihood = {res} -4140.166{txt}  
Iteration 78:{col 16}log likelihood = {res}-4140.1659{txt}  
Iteration 79:{col 16}log likelihood = {res}-4140.1645{txt}  
Iteration 80:{col 16}log likelihood = {res}-4140.1642{txt}  
Iteration 81:{col 16}log likelihood = {res}-4140.1641{txt}  
Iteration 82:{col 16}log likelihood = {res} -4140.164{txt}  
Iteration 83:{col 16}log likelihood = {res}-4140.1635{txt}  
Iteration 84:{col 16}log likelihood = {res}-4140.1624{txt}  
Iteration 85:{col 16}log likelihood = {res}-4140.1619{txt}  
Iteration 86:{col 16}log likelihood = {res}-4140.1616{txt}  
Iteration 87:{col 16}log likelihood = {res}-4140.1612{txt}  
Iteration 88:{col 16}log likelihood = {res}-4140.1608{txt}  
Iteration 89:{col 16}log likelihood = {res}-4140.1603{txt}  
Iteration 90:{col 16}log likelihood = {res}-4140.1597{txt}  
Iteration 91:{col 16}log likelihood = {res}-4140.1595{txt}  
Iteration 92:{col 16}log likelihood = {res}-4140.1593{txt}  
Iteration 93:{col 16}log likelihood = {res}-4140.1592{txt}  
Iteration 94:{col 16}log likelihood = {res}-4140.1586{txt}  
Iteration 95:{col 16}log likelihood = {res}-4140.1585{txt}  
Iteration 96:{col 16}log likelihood = {res}-4140.1582{txt}  
Iteration 97:{col 16}log likelihood = {res}-4140.1581{txt}  
Iteration 98:{col 16}log likelihood = {res} -4140.158{txt}  
Iteration 99:{col 16}log likelihood = {res}-4140.1577{txt}  
Iteration 100:{col 16}log likelihood = {res}-4140.1576{txt}  
Iteration 101:{col 16}log likelihood = {res}-4140.1576{txt}  
Iteration 102:{col 16}log likelihood = {res}-4140.1576{txt}  
Iteration 103:{col 16}log likelihood = {res}-4140.1573{txt}  
Iteration 104:{col 16}log likelihood = {res}-4140.1573{txt}  
Iteration 105:{col 16}log likelihood = {res}-4140.1573{txt}  
Iteration 106:{col 16}log likelihood = {res}-4140.1572{txt}  
Iteration 107:{col 16}log likelihood = {res}-4140.1572{txt}  
Iteration 108:{col 16}log likelihood = {res}-4140.1571{txt}  
Iteration 109:{col 16}log likelihood = {res}-4140.1571{txt}  
Iteration 110:{col 16}log likelihood = {res}-4140.1571{txt}  
Iteration 111:{col 16}log likelihood = {res}-4140.1571{txt}  
Iteration 112:{col 16}log likelihood = {res}-4140.1571{txt}  
Iteration 113:{col 16}log likelihood = {res}-4140.1571{txt}  
Iteration 114:{col 16}log likelihood = {res}-4140.1571{txt}  (backed up)
Iteration 115:{col 16}log likelihood = {res}-4140.1571{txt}  

{col 51}Number of obs{col 67}= {res}      3919
{txt}{col 51}Wald chi2({res}188{txt}){col 67}= {res}  12149.68
{txt}Log likelihood = {res}-4140.1571{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        ln_FGTD{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}mu              {txt}{c |}
{space 8}geddes1 {c |}{col 17}{res}{space 2}-.1536037{col 29}{space 2}  .069558{col 40}{space 1}   -2.21{col 49}{space 3}0.027{col 57}{space 4}-.2899349{col 70}{space 3}-.0172725
{txt}{space 8}geddes2 {c |}{col 17}{res}{space 2}-.0154752{col 29}{space 2} .0594104{col 40}{space 1}   -0.26{col 49}{space 3}0.794{col 57}{space 4}-.1319175{col 70}{space 3}  .100967
{txt}{space 8}geddes3 {c |}{col 17}{res}{space 2}-.0559313{col 29}{space 2} .0751663{col 40}{space 1}   -0.74{col 49}{space 3}0.457{col 57}{space 4}-.2032546{col 70}{space 3} .0913919
{txt}{space 8}geddes4 {c |}{col 17}{res}{space 2}-.0625098{col 29}{space 2} .1841503{col 40}{space 1}   -0.34{col 49}{space 3}0.734{col 57}{space 4}-.4234378{col 70}{space 3} .2984182
{txt}{space 8}geddes5 {c |}{col 17}{res}{space 2}-.2551282{col 29}{space 2} .1780846{col 40}{space 1}   -1.43{col 49}{space 3}0.152{col 57}{space 4}-.6041677{col 70}{space 3} .0939113
{txt}{space 9}logGNI {c |}{col 17}{res}{space 2} -.097709{col 29}{space 2} .0366165{col 40}{space 1}   -2.67{col 49}{space 3}0.008{col 57}{space 4}-.1694759{col 70}{space 3} -.025942
{txt}{space 9}logpop {c |}{col 17}{res}{space 2} .1659509{col 29}{space 2} .0877867{col 40}{space 1}    1.89{col 49}{space 3}0.059{col 57}{space 4}-.0061078{col 70}{space 3} .3380096
{txt}{space 8}logarea {c |}{col 17}{res}{space 2} .0984224{col 29}{space 2} .0544032{col 40}{space 1}    1.81{col 49}{space 3}0.070{col 57}{space 4}-.0082059{col 70}{space 3} .2050507
{txt}{space 11}GINI {c |}{col 17}{res}{space 2} .0030843{col 29}{space 2} .0040887{col 40}{space 1}    0.75{col 49}{space 3}0.451{col 57}{space 4}-.0049295{col 70}{space 3} .0110981
{txt}{space 8}Durable {c |}{col 17}{res}{space 2}-.0019394{col 29}{space 2} .0014684{col 40}{space 1}   -1.32{col 49}{space 3}0.187{col 57}{space 4}-.0048174{col 70}{space 3} .0009385
{txt}{space 10}AggSF {c |}{col 17}{res}{space 2} .0251631{col 29}{space 2} .0117821{col 40}{space 1}    2.14{col 49}{space 3}0.033{col 57}{space 4} .0020705{col 70}{space 3} .0482557
{txt}{space 8}ColdWar {c |}{col 17}{res}{space 2}-.2636564{col 29}{space 2} .1215427{col 40}{space 1}   -2.17{col 49}{space 3}0.030{col 57}{space 4}-.5018757{col 70}{space 3} -.025437
{txt}{space 5}ucdp_type2 {c |}{col 17}{res}{space 2}-.0146222{col 29}{space 2} .0257398{col 40}{space 1}   -0.57{col 49}{space 3}0.570{col 57}{space 4}-.0650712{col 70}{space 3} .0358268
{txt}{space 5}ucdp_type3 {c |}{col 17}{res}{space 2}  .165732{col 29}{space 2} .0247189{col 40}{space 1}    6.70{col 49}{space 3}0.000{col 57}{space 4} .1172838{col 70}{space 3} .2141802
{txt}log_iMigrantsBA {c |}{col 17}{res}{space 2}-.0599346{col 29}{space 2} .0291732{col 40}{space 1}   -2.05{col 49}{space 3}0.040{col 57}{space 4}-.1171131{col 70}{space 3}-.0027561
{txt}{space 11}LDV1 {c |}{col 17}{res}{space 2}   .52534{col 29}{space 2} .0138043{col 40}{space 1}   38.06{col 49}{space 3}0.000{col 57}{space 4} .4982842{col 70}{space 3} .5523959
{txt}{space 4}country_fe2 {c |}{col 17}{res}{space 2}-1.001735{col 29}{space 2}  .294419{col 40}{space 1}   -3.40{col 49}{space 3}0.001{col 57}{space 4}-1.578786{col 70}{space 3}-.4246847
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{txt}{space 2}country_fe109 {c |}{col 17}{res}{space 2}-1.649852{col 29}{space 2} .3159607{col 40}{space 1}   -5.22{col 49}{space 3}0.000{col 57}{space 4}-2.269124{col 70}{space 3}-1.030581
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{txt}{space 2}country_fe112 {c |}{col 17}{res}{space 2}-.8451501{col 29}{space 2} .3294559{col 40}{space 1}   -2.57{col 49}{space 3}0.010{col 57}{space 4}-1.490872{col 70}{space 3}-.1994285
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{txt}{space 2}country_fe114 {c |}{col 17}{res}{space 2} .5523733{col 29}{space 2} .2592585{col 40}{space 1}    2.13{col 49}{space 3}0.033{col 57}{space 4} .0442359{col 70}{space 3} 1.060511
{txt}{space 2}country_fe115 {c |}{col 17}{res}{space 2}-.5674831{col 29}{space 2} .3591999{col 40}{space 1}   -1.58{col 49}{space 3}0.114{col 57}{space 4}-1.271502{col 70}{space 3} .1365358
{txt}{space 2}country_fe116 {c |}{col 17}{res}{space 2} .7859371{col 29}{space 2} .2034919{col 40}{space 1}    3.86{col 49}{space 3}0.000{col 57}{space 4} .3871002{col 70}{space 3} 1.184774
{txt}{space 2}country_fe117 {c |}{col 17}{res}{space 2}-.9274712{col 29}{space 2} .3614924{col 40}{space 1}   -2.57{col 49}{space 3}0.010{col 57}{space 4}-1.635983{col 70}{space 3}-.2189591
{txt}{space 2}country_fe118 {c |}{col 17}{res}{space 2} .0796604{col 29}{space 2} .3238313{col 40}{space 1}    0.25{col 49}{space 3}0.806{col 57}{space 4}-.5550374{col 70}{space 3} .7143581
{txt}{space 2}country_fe119 {c |}{col 17}{res}{space 2}-.0284484{col 29}{space 2} .3985553{col 40}{space 1}   -0.07{col 49}{space 3}0.943{col 57}{space 4}-.8096023{col 70}{space 3} .7527056
{txt}{space 2}country_fe120 {c |}{col 17}{res}{space 2}-.7883778{col 29}{space 2} .4126306{col 40}{space 1}   -1.91{col 49}{space 3}0.056{col 57}{space 4}-1.597119{col 70}{space 3} .0203633
{txt}{space 2}country_fe121 {c |}{col 17}{res}{space 2}-1.627416{col 29}{space 2} .3572203{col 40}{space 1}   -4.56{col 49}{space 3}0.000{col 57}{space 4}-2.327555{col 70}{space 3}-.9272767
{txt}{space 2}country_fe122 {c |}{col 17}{res}{space 2}-1.328901{col 29}{space 2} .4427873{col 40}{space 1}   -3.00{col 49}{space 3}0.003{col 57}{space 4}-2.196748{col 70}{space 3}-.4610533
{txt}{space 2}country_fe123 {c |}{col 17}{res}{space 2}-.8859053{col 29}{space 2} .4040295{col 40}{space 1}   -2.19{col 49}{space 3}0.028{col 57}{space 4}-1.677789{col 70}{space 3}-.0940221
{txt}{space 2}country_fe124 {c |}{col 17}{res}{space 2}-2.298094{col 29}{space 2} .3616817{col 40}{space 1}   -6.35{col 49}{space 3}0.000{col 57}{space 4}-3.006977{col 70}{space 3}-1.589211
{txt}{space 2}country_fe125 {c |}{col 17}{res}{space 2}-1.402423{col 29}{space 2} .4914931{col 40}{space 1}   -2.85{col 49}{space 3}0.004{col 57}{space 4}-2.365732{col 70}{space 3}-.4391142
{txt}{space 2}country_fe126 {c |}{col 17}{res}{space 2}-1.259876{col 29}{space 2} .2568469{col 40}{space 1}   -4.91{col 49}{space 3}0.000{col 57}{space 4}-1.763287{col 70}{space 3}-.7564657
{txt}{space 2}country_fe127 {c |}{col 17}{res}{space 2} -1.15591{col 29}{space 2} .2163943{col 40}{space 1}   -5.34{col 49}{space 3}0.000{col 57}{space 4}-1.580035{col 70}{space 3}-.7317851
{txt}{space 2}country_fe128 {c |}{col 17}{res}{space 2}-.3412301{col 29}{space 2} .2226592{col 40}{space 1}   -1.53{col 49}{space 3}0.125{col 57}{space 4}-.7776341{col 70}{space 3} .0951739
{txt}{space 2}country_fe129 {c |}{col 17}{res}{space 2}-.8622368{col 29}{space 2} .2859106{col 40}{space 1}   -3.02{col 49}{space 3}0.003{col 57}{space 4}-1.422611{col 70}{space 3}-.3018624
{txt}{space 2}country_fe130 {c |}{col 17}{res}{space 2}-.3230732{col 29}{space 2} .2739254{col 40}{space 1}   -1.18{col 49}{space 3}0.238{col 57}{space 4}-.8599572{col 70}{space 3} .2138107
{txt}{space 2}country_fe131 {c |}{col 17}{res}{space 2}-1.522113{col 29}{space 2} .3109223{col 40}{space 1}   -4.90{col 49}{space 3}0.000{col 57}{space 4}-2.131509{col 70}{space 3} -.912716
{txt}{space 2}country_fe132 {c |}{col 17}{res}{space 2}-1.121999{col 29}{space 2} .2309456{col 40}{space 1}   -4.86{col 49}{space 3}0.000{col 57}{space 4}-1.574644{col 70}{space 3}-.6693543
{txt}{space 2}country_fe133 {c |}{col 17}{res}{space 2}-1.260596{col 29}{space 2} .2889644{col 40}{space 1}   -4.36{col 49}{space 3}0.000{col 57}{space 4}-1.826956{col 70}{space 3}-.6942359
{txt}{space 2}country_fe134 {c |}{col 17}{res}{space 2}-.8314532{col 29}{space 2} .2533118{col 40}{space 1}   -3.28{col 49}{space 3}0.001{col 57}{space 4}-1.327935{col 70}{space 3}-.3349712
{txt}{space 2}country_fe135 {c |}{col 17}{res}{space 2}-1.396911{col 29}{space 2} .3333935{col 40}{space 1}   -4.19{col 49}{space 3}0.000{col 57}{space 4} -2.05035{col 70}{space 3}-.7434715
{txt}{space 2}country_fe136 {c |}{col 17}{res}{space 2}-1.452989{col 29}{space 2} .3880642{col 40}{space 1}   -3.74{col 49}{space 3}0.000{col 57}{space 4} -2.21358{col 70}{space 3}-.6923968
{txt}{space 2}country_fe137 {c |}{col 17}{res}{space 2}-.9768992{col 29}{space 2} .2696713{col 40}{space 1}   -3.62{col 49}{space 3}0.000{col 57}{space 4}-1.505445{col 70}{space 3}-.4483532
{txt}{space 2}country_fe139 {c |}{col 17}{res}{space 2}-.3759055{col 29}{space 2} .2555623{col 40}{space 1}   -1.47{col 49}{space 3}0.141{col 57}{space 4}-.8767983{col 70}{space 3} .1249873
{txt}{space 2}country_fe140 {c |}{col 17}{res}{space 2}-1.298012{col 29}{space 2} .3326928{col 40}{space 1}   -3.90{col 49}{space 3}0.000{col 57}{space 4}-1.950078{col 70}{space 3} -.645946
{txt}{space 2}country_fe141 {c |}{col 17}{res}{space 2}  -.77868{col 29}{space 2} .3251485{col 40}{space 1}   -2.39{col 49}{space 3}0.017{col 57}{space 4}-1.415959{col 70}{space 3}-.1414007
{txt}{space 2}country_fe142 {c |}{col 17}{res}{space 2}-1.109927{col 29}{space 2} .3809059{col 40}{space 1}   -2.91{col 49}{space 3}0.004{col 57}{space 4}-1.856489{col 70}{space 3}-.3633657
{txt}{space 2}country_fe143 {c |}{col 17}{res}{space 2}-.6927216{col 29}{space 2} .2869399{col 40}{space 1}   -2.41{col 49}{space 3}0.016{col 57}{space 4}-1.255113{col 70}{space 3}-.1303297
{txt}{space 2}country_fe144 {c |}{col 17}{res}{space 2}-.8611433{col 29}{space 2} .4514212{col 40}{space 1}   -1.91{col 49}{space 3}0.056{col 57}{space 4}-1.745913{col 70}{space 3}  .023626
{txt}{space 2}country_fe145 {c |}{col 17}{res}{space 2}-.7641339{col 29}{space 2} .4190116{col 40}{space 1}   -1.82{col 49}{space 3}0.068{col 57}{space 4}-1.585382{col 70}{space 3} .0571137
{txt}{space 7}year_fe2 {c |}{col 17}{res}{space 2} -.003388{col 29}{space 2} .0923829{col 40}{space 1}   -0.04{col 49}{space 3}0.971{col 57}{space 4}-.1844551{col 70}{space 3}  .177679
{txt}{space 7}year_fe3 {c |}{col 17}{res}{space 2} .1409142{col 29}{space 2}  .092106{col 40}{space 1}    1.53{col 49}{space 3}0.126{col 57}{space 4}-.0396102{col 70}{space 3} .3214386
{txt}{space 7}year_fe4 {c |}{col 17}{res}{space 2} .1168121{col 29}{space 2} .0929476{col 40}{space 1}    1.26{col 49}{space 3}0.209{col 57}{space 4}-.0653619{col 70}{space 3} .2989861
{txt}{space 7}year_fe5 {c |}{col 17}{res}{space 2} .1648797{col 29}{space 2} .0944846{col 40}{space 1}    1.75{col 49}{space 3}0.081{col 57}{space 4}-.0203066{col 70}{space 3} .3500661
{txt}{space 7}year_fe6 {c |}{col 17}{res}{space 2} .2317919{col 29}{space 2} .0955504{col 40}{space 1}    2.43{col 49}{space 3}0.015{col 57}{space 4} .0445166{col 70}{space 3} .4190672
{txt}{space 7}year_fe7 {c |}{col 17}{res}{space 2} .2162662{col 29}{space 2}  .096558{col 40}{space 1}    2.24{col 49}{space 3}0.025{col 57}{space 4} .0270159{col 70}{space 3} .4055164
{txt}{space 7}year_fe8 {c |}{col 17}{res}{space 2} .3791466{col 29}{space 2} .0982239{col 40}{space 1}    3.86{col 49}{space 3}0.000{col 57}{space 4} .1866313{col 70}{space 3}  .571662
{txt}{space 7}year_fe9 {c |}{col 17}{res}{space 2} .4800036{col 29}{space 2} .1003456{col 40}{space 1}    4.78{col 49}{space 3}0.000{col 57}{space 4} .2833297{col 70}{space 3} .6766774
{txt}{space 6}year_fe10 {c |}{col 17}{res}{space 2} .3455725{col 29}{space 2} .1031885{col 40}{space 1}    3.35{col 49}{space 3}0.001{col 57}{space 4} .1433267{col 70}{space 3} .5478182
{txt}{space 6}year_fe11 {c |}{col 17}{res}{space 2} .3495202{col 29}{space 2} .1056576{col 40}{space 1}    3.31{col 49}{space 3}0.001{col 57}{space 4} .1424352{col 70}{space 3} .5566053
{txt}{space 6}year_fe12 {c |}{col 17}{res}{space 2} .2199062{col 29}{space 2} .1055686{col 40}{space 1}    2.08{col 49}{space 3}0.037{col 57}{space 4} .0129955{col 70}{space 3} .4268168
{txt}{space 6}year_fe13 {c |}{col 17}{res}{space 2} .4272864{col 29}{space 2} .1041858{col 40}{space 1}    4.10{col 49}{space 3}0.000{col 57}{space 4} .2230859{col 70}{space 3} .6314869
{txt}{space 6}year_fe14 {c |}{col 17}{res}{space 2}  .335956{col 29}{space 2} .1047583{col 40}{space 1}    3.21{col 49}{space 3}0.001{col 57}{space 4} .1306335{col 70}{space 3} .5412784
{txt}{space 6}year_fe15 {c |}{col 17}{res}{space 2} .3176463{col 29}{space 2} .1049022{col 40}{space 1}    3.03{col 49}{space 3}0.002{col 57}{space 4} .1120418{col 70}{space 3} .5232507
{txt}{space 6}year_fe16 {c |}{col 17}{res}{space 2} .3635264{col 29}{space 2} .1047983{col 40}{space 1}    3.47{col 49}{space 3}0.001{col 57}{space 4} .1581255{col 70}{space 3} .5689274
{txt}{space 6}year_fe17 {c |}{col 17}{res}{space 2} .3590292{col 29}{space 2}  .106891{col 40}{space 1}    3.36{col 49}{space 3}0.001{col 57}{space 4} .1495267{col 70}{space 3} .5685317
{txt}{space 6}year_fe18 {c |}{col 17}{res}{space 2} .4698388{col 29}{space 2} .1082165{col 40}{space 1}    4.34{col 49}{space 3}0.000{col 57}{space 4} .2577384{col 70}{space 3} .6819392
{txt}{space 6}year_fe19 {c |}{col 17}{res}{space 2} .5379231{col 29}{space 2} .1107722{col 40}{space 1}    4.86{col 49}{space 3}0.000{col 57}{space 4} .3208136{col 70}{space 3} .7550326
{txt}{space 6}year_fe20 {c |}{col 17}{res}{space 2} .4001683{col 29}{space 2} .1118877{col 40}{space 1}    3.58{col 49}{space 3}0.000{col 57}{space 4} .1808724{col 70}{space 3} .6194642
{txt}{space 6}year_fe21 {c |}{col 17}{res}{space 2} .7527045{col 29}{space 2} .1137195{col 40}{space 1}    6.62{col 49}{space 3}0.000{col 57}{space 4} .5298184{col 70}{space 3} .9755906
{txt}{space 6}year_fe22 {c |}{col 17}{res}{space 2} .6668337{col 29}{space 2} .0889433{col 40}{space 1}    7.50{col 49}{space 3}0.000{col 57}{space 4} .4925079{col 70}{space 3} .8411594
{txt}{space 6}year_fe23 {c |}{col 17}{res}{space 2} 1.672799{col 29}{space 2} .0890196{col 40}{space 1}   18.79{col 49}{space 3}0.000{col 57}{space 4} 1.498323{col 70}{space 3} 1.847274
{txt}{space 6}year_fe24 {c |}{col 17}{res}{space 2}-.5380438{col 29}{space 2}  .095917{col 40}{space 1}   -5.61{col 49}{space 3}0.000{col 57}{space 4}-.7260377{col 70}{space 3}-.3500499
{txt}{space 6}year_fe25 {c |}{col 17}{res}{space 2}  .074644{col 29}{space 2} .0870932{col 40}{space 1}    0.86{col 49}{space 3}0.391{col 57}{space 4}-.0960555{col 70}{space 3} .2453435
{txt}{space 6}year_fe26 {c |}{col 17}{res}{space 2} .1561917{col 29}{space 2} .0861991{col 40}{space 1}    1.81{col 49}{space 3}0.070{col 57}{space 4}-.0127553{col 70}{space 3} .3251388
{txt}{space 6}year_fe27 {c |}{col 17}{res}{space 2} .2299922{col 29}{space 2} .0856169{col 40}{space 1}    2.69{col 49}{space 3}0.007{col 57}{space 4} .0621861{col 70}{space 3} .3977982
{txt}{space 6}year_fe28 {c |}{col 17}{res}{space 2}-.3732689{col 29}{space 2} .0854819{col 40}{space 1}   -4.37{col 49}{space 3}0.000{col 57}{space 4}-.5408103{col 70}{space 3}-.2057275
{txt}{space 6}year_fe29 {c |}{col 17}{res}{space 2}  .092072{col 29}{space 2} .0844029{col 40}{space 1}    1.09{col 49}{space 3}0.275{col 57}{space 4}-.0733547{col 70}{space 3} .2574987
{txt}{space 6}year_fe30 {c |}{col 17}{res}{space 2}-.0277496{col 29}{space 2} .0843145{col 40}{space 1}   -0.33{col 49}{space 3}0.742{col 57}{space 4}-.1930031{col 70}{space 3} .1375038
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .8198648{col 29}{space 2} .5745891{col 40}{space 1}    1.43{col 49}{space 3}0.154{col 57}{space 4} -.306309{col 70}{space 3} 1.946039
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}rho             {txt}{c |}
{space 10}_cons {c |}{col 17}{res}{space 2}  .081162{col 29}{space 2}  .016167{col 40}{space 1}    5.02{col 49}{space 3}0.000{col 57}{space 4} .0494752{col 70}{space 3} .1128488
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}sigma           {txt}{c |}
{space 10}_cons {c |}{col 17}{res}{space 2} .6956212{col 29}{space 2} .0078582{col 40}{space 1}   88.52{col 49}{space 3}0.000{col 57}{space 4} .6802194{col 70}{space 3}  .711023
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. ***************************************************
. * m-STAR Model 1 - Migrant Inflows and Contiguity *
. ***************************************************
. 
. clear
{txt}
{com}. pr drop _all
{txt}
{com}. set more off
{txt}
{com}. set matsize 8000
{txt}
{com}. 
. *  Likelihood Evaluator *                                                          
. 
. program define splag_ll
{txt}  1{com}. 
. args lnf mu rho1 rho2 sigma
{txt}  2{com}. tempvar A rSL1 rSL2 
{txt}  3{com}. gen double `rSL1'=`rho1'*SL1
{txt}  4{com}. gen double `rSL2'=`rho2'*SL2
{txt}  5{com}. scalar p1 = `rho1'
{txt}  6{com}. scalar p2 = `rho2'
{txt}  7{com}. matrix p1W1 = p1*W1
{txt}  8{com}. matrix p2W2 = p2*W2
{txt}  9{com}. matrix IpW = I_n - p1W1 - p2W2
{txt} 10{com}. qui gen double `A' = ln(det(IpW))/$nobs if _n == 1
{txt} 11{com}. scalar A = `A'
{txt} 12{com}. qui replace `lnf'= A + ln(normalden($ML_y1-`rSL1'-`rSL2'-`mu', 0, `sigma'))
{txt} 13{com}. end
{txt}
{com}. 
. * Open Data For Weights *
. 
. spatwmat using "Matrix.dta", name(W1) standardize 


{txt}The following matrix has been created:

1. Imported non-binary weights matrix {res}W1{txt} (row-standardized)
   Dimension: {res}3919x3919

{err}   Beware! 1 location has no neighbors


{txt}
{com}. 
. spatwmat using "Contiguity Matrix.dta", name(W2) standardize 


{txt}The following matrix has been created:

1. Imported binary weights matrix {res}W2{txt} (row-standardized)
   Dimension: {res}3919x3919

{err}   Beware! 497 locations have no neighbors


{txt}
{com}. 
. * Open Data for Regression *
. 
. drop _all
{txt}
{com}. use "Data Set I.dta", clear
{txt}
{com}. global nobs = 3919
{txt}
{com}. matrix I_n = I($nobs)
{txt}
{com}. global Y ln_FGTD
{txt}
{com}. global X geddes1-geddes5 logGNI logpop logarea GINI Durable AggSF ColdWar ucdp_type2 ucdp_type3 log_iMigrantsBA LDV1 country_fe2-country_fe145 year_fe2-year_fe31
{txt}
{com}. global Z LDV1
{txt}
{com}. mkmat $Y, matrix(Y)
{res}{txt}
{com}. mkmat $Z, matrix(Z)
{res}{txt}
{com}. matrix SL1 = W1*Z
{txt}
{com}. svmat SL1, n(SL1)
{txt}
{com}. matrix SL2 = W2*Z
{txt}
{com}. svmat SL2, n(SL2)
{txt}
{com}. 
. * Produce Starting Values * 
. 
. qui regress $Y $X
{txt}
{com}. matrix OLSb=e(b)
{txt}
{com}. local OLSsigma=e(rmse)
{txt}
{com}. 
. * Estimate Spatial Lag Model *
. 
. ml model lf splag_ll (mu: $Y=$X) (rho1:) (rho2:) (sigma:)
{txt}note: country_fe138 dropped because of collinearity
note: year_fe31 dropped because of collinearity

{com}. ml init OLSb, skip
{txt}
{com}. ml init rho1:_cons=0
{txt}
{com}. ml init rho2:_cons=0
{txt}
{com}. ml init sigma:_cons=`OLSsigma'
{txt}
{com}. ml max

{txt}initial:       log likelihood = {res}-1766.5604
{txt}rescale:       log likelihood = {res}-1766.5604
{txt}rescale eq:    log likelihood = {res}-1766.5604
{txt}Iteration 0:{col 16}log likelihood = {res}-1766.5604{txt}  
Iteration 1:{col 16}log likelihood = {res}-1738.9863{txt}  
Iteration 2:{col 16}log likelihood = {res} -1738.415{txt}  
Iteration 3:{col 16}log likelihood = {res}-1738.4147{txt}  
Iteration 4:{col 16}log likelihood = {res}-1738.4147{txt}  

{col 51}Number of obs{col 67}= {res}      3919
{txt}{col 51}Wald chi2({res}188{txt}){col 67}= {res}  11626.01
{txt}Log likelihood = {res}-1738.4147{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        ln_FGTD{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}mu              {txt}{c |}
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{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}rho1            {txt}{c |}
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{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
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{space 10}_cons {c |}{col 17}{res}{space 2} .1019557{col 29}{space 2} .0202923{col 40}{space 1}    5.02{col 49}{space 3}0.000{col 57}{space 4} .0621835{col 70}{space 3} .1417278
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}sigma           {txt}{c |}
{space 10}_cons {c |}{col 17}{res}{space 2} .6933345{col 29}{space 2} .0078314{col 40}{space 1}   88.53{col 49}{space 3}0.000{col 57}{space 4} .6779852{col 70}{space 3} .7086837
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *************************************************
. * m-STAR Model 2 - Migrant Inflows and Distance *
. *************************************************
. 
. clear
{txt}
{com}. pr drop _all
{txt}
{com}. set more off
{txt}
{com}. set matsize 8000
{txt}
{com}. 
. *  Likelihood Evaluator *                                                          
. 
. program define splag_ll
{txt}  1{com}. 
. args lnf mu rho1 rho2 sigma
{txt}  2{com}. tempvar A rSL1 rSL2 
{txt}  3{com}. gen double `rSL1'=`rho1'*SL1
{txt}  4{com}. gen double `rSL2'=`rho2'*SL2
{txt}  5{com}. scalar p1 = `rho1'
{txt}  6{com}. scalar p2 = `rho2'
{txt}  7{com}. matrix p1W1 = p1*W1
{txt}  8{com}. matrix p2W2 = p2*W2
{txt}  9{com}. matrix IpW = I_n - p1W1 - p2W2
{txt} 10{com}. qui gen double `A' = ln(det(IpW))/$nobs if _n == 1
{txt} 11{com}. scalar A = `A'
{txt} 12{com}. qui replace `lnf'= A + ln(normalden($ML_y1-`rSL1'-`rSL2'-`mu', 0, `sigma'))
{txt} 13{com}. end
{txt}
{com}. 
. * Open Data For Weights *
. 
. spatwmat using "Matrix.dta", name(W1) standardize 


{txt}The following matrix has been created:

1. Imported non-binary weights matrix {res}W1{txt} (row-standardized)
   Dimension: {res}3919x3919

{err}   Beware! 1 location has no neighbors


{txt}
{com}. 
. spatwmat using "Inv_Distance Matrix.dta", name(W2) standardize 


{txt}The following matrix has been created:

1. Imported non-binary weights matrix {res}W2{txt} (row-standardized)
   Dimension: {res}3919x3919


{txt}
{com}. 
. * Open Data for Regression *
. 
. drop _all
{txt}
{com}. use "Data Set I.dta", clear
{txt}
{com}. global nobs = 3919
{txt}
{com}. matrix I_n = I($nobs)
{txt}
{com}. global Y ln_FGTD
{txt}
{com}. global X geddes1-geddes5 logGNI logpop logarea GINI Durable AggSF ColdWar ucdp_type2 ucdp_type3 log_iMigrantsBA LDV1 country_fe2-country_fe145 year_fe2-year_fe31
{txt}
{com}. global Z LDV1
{txt}
{com}. mkmat $Y, matrix(Y)
{res}{txt}
{com}. mkmat $Z, matrix(Z)
{res}{txt}
{com}. matrix SL1 = W1*Z
{txt}
{com}. svmat SL1, n(SL1)
{txt}
{com}. matrix SL2 = W2*Z
{txt}
{com}. svmat SL2, n(SL2)
{txt}
{com}. 
. * Produce Starting Values * 
. 
. qui regress $Y $X
{txt}
{com}. matrix OLSb=e(b)
{txt}
{com}. local OLSsigma=e(rmse)
{txt}
{com}. 
. * Estimate Spatial Lag Model *
. 
. ml model lf splag_ll (mu: $Y=$X) (rho1:) (rho2:) (sigma:)
{txt}note: country_fe138 dropped because of collinearity
note: year_fe31 dropped because of collinearity

{com}. ml init OLSb, skip
{txt}
{com}. ml init rho1:_cons=0
{txt}
{com}. ml init rho2:_cons=0
{txt}
{com}. ml init sigma:_cons=`OLSsigma'
{txt}
{com}. ml max

{txt}initial:       log likelihood = {res}-1766.5604
{txt}rescale:       log likelihood = {res}-1766.5604
{txt}rescale eq:    log likelihood = {res}-1766.5604
{txt}Iteration 0:{col 16}log likelihood = {res}-1766.5604{txt}  
Iteration 1:{col 16}log likelihood = {res}-1742.3517{txt}  
Iteration 2:{col 16}log likelihood = {res}-1741.9078{txt}  
Iteration 3:{col 16}log likelihood = {res}-1741.9075{txt}  
Iteration 4:{col 16}log likelihood = {res}-1741.9075{txt}  

{col 51}Number of obs{col 67}= {res}      3919
{txt}{col 51}Wald chi2({res}188{txt}){col 67}= {res}  11822.15
{txt}Log likelihood = {res}-1741.9075{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        ln_FGTD{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}mu              {txt}{c |}
{space 8}geddes1 {c |}{col 17}{res}{space 2}-.1535818{col 29}{space 2} .0694009{col 40}{space 1}   -2.21{col 49}{space 3}0.027{col 57}{space 4} -.289605{col 70}{space 3}-.0175585
{txt}{space 8}geddes2 {c |}{col 17}{res}{space 2}-.0319521{col 29}{space 2} .0593942{col 40}{space 1}   -0.54{col 49}{space 3}0.591{col 57}{space 4}-.1483627{col 70}{space 3} .0844585
{txt}{space 8}geddes3 {c |}{col 17}{res}{space 2}-.0542195{col 29}{space 2} .0750042{col 40}{space 1}   -0.72{col 49}{space 3}0.470{col 57}{space 4} -.201225{col 70}{space 3} .0927859
{txt}{space 8}geddes4 {c |}{col 17}{res}{space 2}-.0506259{col 29}{space 2} .1838822{col 40}{space 1}   -0.28{col 49}{space 3}0.783{col 57}{space 4}-.4110283{col 70}{space 3} .3097765
{txt}{space 8}geddes5 {c |}{col 17}{res}{space 2}-.2471177{col 29}{space 2} .1776689{col 40}{space 1}   -1.39{col 49}{space 3}0.164{col 57}{space 4}-.5953425{col 70}{space 3}  .101107
{txt}{space 9}logGNI {c |}{col 17}{res}{space 2}-.0959715{col 29}{space 2} .0365575{col 40}{space 1}   -2.63{col 49}{space 3}0.009{col 57}{space 4}-.1676228{col 70}{space 3}-.0243202
{txt}{space 9}logpop {c |}{col 17}{res}{space 2} .1600618{col 29}{space 2} .0876665{col 40}{space 1}    1.83{col 49}{space 3}0.068{col 57}{space 4}-.0117613{col 70}{space 3} .3318849
{txt}{space 8}logarea {c |}{col 17}{res}{space 2} .0859984{col 29}{space 2} .0543824{col 40}{space 1}    1.58{col 49}{space 3}0.114{col 57}{space 4}-.0205892{col 70}{space 3}  .192586
{txt}{space 11}GINI {c |}{col 17}{res}{space 2}  .004277{col 29}{space 2} .0040875{col 40}{space 1}    1.05{col 49}{space 3}0.295{col 57}{space 4}-.0037343{col 70}{space 3} .0122883
{txt}{space 8}Durable {c |}{col 17}{res}{space 2}-.0017167{col 29}{space 2} .0014658{col 40}{space 1}   -1.17{col 49}{space 3}0.242{col 57}{space 4}-.0045896{col 70}{space 3} .0011563
{txt}{space 10}AggSF {c |}{col 17}{res}{space 2} .0274024{col 29}{space 2} .0117758{col 40}{space 1}    2.33{col 49}{space 3}0.020{col 57}{space 4} .0043222{col 70}{space 3} .0504826
{txt}{space 8}ColdWar {c |}{col 17}{res}{space 2} .2895944{col 29}{space 2} .1753448{col 40}{space 1}    1.65{col 49}{space 3}0.099{col 57}{space 4} -.054075{col 70}{space 3} .6332639
{txt}{space 5}ucdp_type2 {c |}{col 17}{res}{space 2}-.0098622{col 29}{space 2} .0257169{col 40}{space 1}   -0.38{col 49}{space 3}0.701{col 57}{space 4}-.0602664{col 70}{space 3} .0405419
{txt}{space 5}ucdp_type3 {c |}{col 17}{res}{space 2} .1659492{col 29}{space 2} .0246597{col 40}{space 1}    6.73{col 49}{space 3}0.000{col 57}{space 4} .1176171{col 70}{space 3} .2142813
{txt}log_iMigrantsBA {c |}{col 17}{res}{space 2} -.065799{col 29}{space 2} .0291316{col 40}{space 1}   -2.26{col 49}{space 3}0.024{col 57}{space 4}-.1228959{col 70}{space 3}-.0087022
{txt}{space 11}LDV1 {c |}{col 17}{res}{space 2} .5215967{col 29}{space 2} .0138157{col 40}{space 1}   37.75{col 49}{space 3}0.000{col 57}{space 4} .4945184{col 70}{space 3}  .548675
{txt}{space 4}country_fe2 {c |}{col 17}{res}{space 2}-1.028061{col 29}{space 2} .2941074{col 40}{space 1}   -3.50{col 49}{space 3}0.000{col 57}{space 4}-1.604501{col 70}{space 3}-.4516209
{txt}{space 4}country_fe3 {c |}{col 17}{res}{space 2}-1.223375{col 29}{space 2}  .267076{col 40}{space 1}   -4.58{col 49}{space 3}0.000{col 57}{space 4}-1.746835{col 70}{space 3} -.699916
{txt}{space 4}country_fe4 {c |}{col 17}{res}{space 2}-.8202018{col 29}{space 2} .2789223{col 40}{space 1}   -2.94{col 49}{space 3}0.003{col 57}{space 4}-1.366879{col 70}{space 3}-.2735242
{txt}{space 4}country_fe5 {c |}{col 17}{res}{space 2}-.8115852{col 29}{space 2} .2368744{col 40}{space 1}   -3.43{col 49}{space 3}0.001{col 57}{space 4}-1.275851{col 70}{space 3}  -.34732
{txt}{space 4}country_fe6 {c |}{col 17}{res}{space 2} -.852698{col 29}{space 2} .2567693{col 40}{space 1}   -3.32{col 49}{space 3}0.001{col 57}{space 4}-1.355957{col 70}{space 3}-.3494393
{txt}{space 4}country_fe7 {c |}{col 17}{res}{space 2}-.6627749{col 29}{space 2} .2673492{col 40}{space 1}   -2.48{col 49}{space 3}0.013{col 57}{space 4} -1.18677{col 70}{space 3}-.1387802
{txt}{space 4}country_fe8 {c |}{col 17}{res}{space 2}-.3315198{col 29}{space 2}  .301183{col 40}{space 1}   -1.10{col 49}{space 3}0.271{col 57}{space 4}-.9218277{col 70}{space 3}  .258788
{txt}{space 4}country_fe9 {c |}{col 17}{res}{space 2}-1.030939{col 29}{space 2} .3036838{col 40}{space 1}   -3.39{col 49}{space 3}0.001{col 57}{space 4}-1.626148{col 70}{space 3}-.4357295
{txt}{space 3}country_fe10 {c |}{col 17}{res}{space 2}-.2797198{col 29}{space 2} .4926757{col 40}{space 1}   -0.57{col 49}{space 3}0.570{col 57}{space 4}-1.245346{col 70}{space 3} .6859069
{txt}{space 3}country_fe11 {c |}{col 17}{res}{space 2}-.6469228{col 29}{space 2} .2780949{col 40}{space 1}   -2.33{col 49}{space 3}0.020{col 57}{space 4}-1.191979{col 70}{space 3}-.1018669
{txt}{space 3}country_fe12 {c |}{col 17}{res}{space 2} -.715822{col 29}{space 2} .3098769{col 40}{space 1}   -2.31{col 49}{space 3}0.021{col 57}{space 4} -1.32317{col 70}{space 3}-.1084744
{txt}{space 3}country_fe13 {c |}{col 17}{res}{space 2} .0595103{col 29}{space 2} .2478015{col 40}{space 1}    0.24{col 49}{space 3}0.810{col 57}{space 4}-.4261718{col 70}{space 3} .5451924
{txt}{space 3}country_fe14 {c |}{col 17}{res}{space 2}-.2662655{col 29}{space 2} .3282342{col 40}{space 1}   -0.81{col 49}{space 3}0.417{col 57}{space 4}-.9095926{col 70}{space 3} .3770616
{txt}{space 3}country_fe15 {c |}{col 17}{res}{space 2}-.6731918{col 29}{space 2} .2716645{col 40}{space 1}   -2.48{col 49}{space 3}0.013{col 57}{space 4}-1.205644{col 70}{space 3}-.1407392
{txt}{space 3}country_fe16 {c |}{col 17}{res}{space 2}-.7098972{col 29}{space 2} .3237287{col 40}{space 1}   -2.19{col 49}{space 3}0.028{col 57}{space 4}-1.344394{col 70}{space 3}-.0754006
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{txt}{space 3}country_fe18 {c |}{col 17}{res}{space 2}-.6430473{col 29}{space 2} .2847065{col 40}{space 1}   -2.26{col 49}{space 3}0.024{col 57}{space 4}-1.201062{col 70}{space 3}-.0850327
{txt}{space 3}country_fe19 {c |}{col 17}{res}{space 2} -1.14895{col 29}{space 2}  .469002{col 40}{space 1}   -2.45{col 49}{space 3}0.014{col 57}{space 4}-2.068177{col 70}{space 3} -.229723
{txt}{space 3}country_fe20 {c |}{col 17}{res}{space 2}-.6129674{col 29}{space 2} .4888487{col 40}{space 1}   -1.25{col 49}{space 3}0.210{col 57}{space 4}-1.571093{col 70}{space 3} .3451585
{txt}{space 3}country_fe21 {c |}{col 17}{res}{space 2}-.7896874{col 29}{space 2} .2996006{col 40}{space 1}   -2.64{col 49}{space 3}0.008{col 57}{space 4}-1.376894{col 70}{space 3} -.202481
{txt}{space 3}country_fe22 {c |}{col 17}{res}{space 2}-.4197515{col 29}{space 2} .3372293{col 40}{space 1}   -1.24{col 49}{space 3}0.213{col 57}{space 4}-1.080709{col 70}{space 3} .2412059
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{txt}{space 3}country_fe24 {c |}{col 17}{res}{space 2}-.8828603{col 29}{space 2} .3813098{col 40}{space 1}   -2.32{col 49}{space 3}0.021{col 57}{space 4}-1.630214{col 70}{space 3}-.1355069
{txt}{space 3}country_fe25 {c |}{col 17}{res}{space 2}-1.014221{col 29}{space 2} .3707713{col 40}{space 1}   -2.74{col 49}{space 3}0.006{col 57}{space 4}-1.740919{col 70}{space 3}-.2875225
{txt}{space 3}country_fe26 {c |}{col 17}{res}{space 2}-.2084187{col 29}{space 2} .3205747{col 40}{space 1}   -0.65{col 49}{space 3}0.516{col 57}{space 4}-.8367335{col 70}{space 3} .4198961
{txt}{space 3}country_fe27 {c |}{col 17}{res}{space 2}-.4157994{col 29}{space 2} .3144992{col 40}{space 1}   -1.32{col 49}{space 3}0.186{col 57}{space 4}-1.032207{col 70}{space 3} .2006077
{txt}{space 3}country_fe28 {c |}{col 17}{res}{space 2}-1.075629{col 29}{space 2} .3308556{col 40}{space 1}   -3.25{col 49}{space 3}0.001{col 57}{space 4}-1.724095{col 70}{space 3}-.4271644
{txt}{space 3}country_fe29 {c |}{col 17}{res}{space 2} .1831195{col 29}{space 2} .1622335{col 40}{space 1}    1.13{col 49}{space 3}0.259{col 57}{space 4}-.1348523{col 70}{space 3} .5010913
{txt}{space 3}country_fe30 {c |}{col 17}{res}{space 2}-.3066156{col 29}{space 2} .2480351{col 40}{space 1}   -1.24{col 49}{space 3}0.216{col 57}{space 4}-.7927554{col 70}{space 3} .1795242
{txt}{space 3}country_fe31 {c |}{col 17}{res}{space 2}-.2373928{col 29}{space 2} .1758592{col 40}{space 1}   -1.35{col 49}{space 3}0.177{col 57}{space 4}-.5820706{col 70}{space 3} .1072849
{txt}{space 3}country_fe32 {c |}{col 17}{res}{space 2}-.1811658{col 29}{space 2} .1792839{col 40}{space 1}   -1.01{col 49}{space 3}0.312{col 57}{space 4}-.5325559{col 70}{space 3} .1702243
{txt}{space 3}country_fe33 {c |}{col 17}{res}{space 2}-.0827095{col 29}{space 2} .2971694{col 40}{space 1}   -0.28{col 49}{space 3}0.781{col 57}{space 4}-.6651508{col 70}{space 3} .4997318
{txt}{space 3}country_fe34 {c |}{col 17}{res}{space 2} .4542345{col 29}{space 2} .2316992{col 40}{space 1}    1.96{col 49}{space 3}0.050{col 57}{space 4} .0001123{col 70}{space 3} .9083566
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{txt}{space 3}country_fe36 {c |}{col 17}{res}{space 2} .7525751{col 29}{space 2} .2404503{col 40}{space 1}    3.13{col 49}{space 3}0.002{col 57}{space 4} .2813013{col 70}{space 3} 1.223849
{txt}{space 3}country_fe37 {c |}{col 17}{res}{space 2}-.6112236{col 29}{space 2} .2292719{col 40}{space 1}   -2.67{col 49}{space 3}0.008{col 57}{space 4}-1.060588{col 70}{space 3}-.1618591
{txt}{space 3}country_fe38 {c |}{col 17}{res}{space 2} .2682439{col 29}{space 2} .2990422{col 40}{space 1}    0.90{col 49}{space 3}0.370{col 57}{space 4}-.3178682{col 70}{space 3} .8543559
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{txt}{space 3}country_fe41 {c |}{col 17}{res}{space 2}-.9917823{col 29}{space 2}  .244506{col 40}{space 1}   -4.06{col 49}{space 3}0.000{col 57}{space 4}-1.471005{col 70}{space 3}-.5125594
{txt}{space 3}country_fe42 {c |}{col 17}{res}{space 2}-.8923324{col 29}{space 2} .3181634{col 40}{space 1}   -2.80{col 49}{space 3}0.005{col 57}{space 4}-1.515921{col 70}{space 3}-.2687437
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{txt}{space 3}country_fe44 {c |}{col 17}{res}{space 2} .2171599{col 29}{space 2} .1987399{col 40}{space 1}    1.09{col 49}{space 3}0.275{col 57}{space 4}-.1723632{col 70}{space 3}  .606683
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{txt}{space 3}country_fe47 {c |}{col 17}{res}{space 2}-.2659676{col 29}{space 2} .3693245{col 40}{space 1}   -0.72{col 49}{space 3}0.471{col 57}{space 4}-.9898303{col 70}{space 3} .4578951
{txt}{space 3}country_fe48 {c |}{col 17}{res}{space 2} .3537867{col 29}{space 2}  .242178{col 40}{space 1}    1.46{col 49}{space 3}0.144{col 57}{space 4}-.1208735{col 70}{space 3} .8284469
{txt}{space 3}country_fe49 {c |}{col 17}{res}{space 2} .1328841{col 29}{space 2}  .331329{col 40}{space 1}    0.40{col 49}{space 3}0.688{col 57}{space 4}-.5165088{col 70}{space 3} .7822769
{txt}{space 3}country_fe50 {c |}{col 17}{res}{space 2}-1.209284{col 29}{space 2} .2863099{col 40}{space 1}   -4.22{col 49}{space 3}0.000{col 57}{space 4}-1.770441{col 70}{space 3}-.6481264
{txt}{space 3}country_fe51 {c |}{col 17}{res}{space 2}-1.799427{col 29}{space 2}  .442066{col 40}{space 1}   -4.07{col 49}{space 3}0.000{col 57}{space 4}-2.665861{col 70}{space 3}-.9329938
{txt}{space 3}country_fe52 {c |}{col 17}{res}{space 2}-.7955917{col 29}{space 2}  .335401{col 40}{space 1}   -2.37{col 49}{space 3}0.018{col 57}{space 4}-1.452966{col 70}{space 3}-.1382177
{txt}{space 3}country_fe53 {c |}{col 17}{res}{space 2}-.6991476{col 29}{space 2} .3332549{col 40}{space 1}   -2.10{col 49}{space 3}0.036{col 57}{space 4}-1.352315{col 70}{space 3}  -.04598
{txt}{space 3}country_fe54 {c |}{col 17}{res}{space 2}-1.132611{col 29}{space 2} .3169095{col 40}{space 1}   -3.57{col 49}{space 3}0.000{col 57}{space 4}-1.753743{col 70}{space 3}-.5114802
{txt}{space 3}country_fe55 {c |}{col 17}{res}{space 2} .0704509{col 29}{space 2} .3711976{col 40}{space 1}    0.19{col 49}{space 3}0.849{col 57}{space 4} -.657083{col 70}{space 3} .7979848
{txt}{space 3}country_fe56 {c |}{col 17}{res}{space 2} -.666166{col 29}{space 2} .3356358{col 40}{space 1}   -1.98{col 49}{space 3}0.047{col 57}{space 4}   -1.324{col 70}{space 3}-.0083319
{txt}{space 3}country_fe57 {c |}{col 17}{res}{space 2}-1.268166{col 29}{space 2} .3598763{col 40}{space 1}   -3.52{col 49}{space 3}0.000{col 57}{space 4}-1.973511{col 70}{space 3}-.5628216
{txt}{space 3}country_fe58 {c |}{col 17}{res}{space 2}-1.283844{col 29}{space 2} .4504845{col 40}{space 1}   -2.85{col 49}{space 3}0.004{col 57}{space 4}-2.166778{col 70}{space 3} -.400911
{txt}{space 3}country_fe59 {c |}{col 17}{res}{space 2}-.8822575{col 29}{space 2} .3151196{col 40}{space 1}   -2.80{col 49}{space 3}0.005{col 57}{space 4}-1.499881{col 70}{space 3}-.2646345
{txt}{space 3}country_fe60 {c |}{col 17}{res}{space 2}-.5391589{col 29}{space 2} .2497765{col 40}{space 1}   -2.16{col 49}{space 3}0.031{col 57}{space 4}-1.028712{col 70}{space 3}-.0496059
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{txt}{space 6}year_fe19 {c |}{col 17}{res}{space 2}-.2784996{col 29}{space 2} .2175824{col 40}{space 1}   -1.28{col 49}{space 3}0.201{col 57}{space 4}-.7049534{col 70}{space 3} .1479541
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{txt}{space 6}year_fe22 {c |}{col 17}{res}{space 2} .0451694{col 29}{space 2} .1689834{col 40}{space 1}    0.27{col 49}{space 3}0.789{col 57}{space 4} -.286032{col 70}{space 3} .3763707
{txt}{space 6}year_fe23 {c |}{col 17}{res}{space 2} .6157872{col 29}{space 2} .2598006{col 40}{space 1}    2.37{col 49}{space 3}0.018{col 57}{space 4} .1065875{col 70}{space 3} 1.124987
{txt}{space 6}year_fe24 {c |}{col 17}{res}{space 2} -2.86058{col 29}{space 2} .5462283{col 40}{space 1}   -5.24{col 49}{space 3}0.000{col 57}{space 4}-3.931168{col 70}{space 3}-1.789992
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{txt}{space 6}year_fe28 {c |}{col 17}{res}{space 2}-.8700883{col 29}{space 2} .1428475{col 40}{space 1}   -6.09{col 49}{space 3}0.000{col 57}{space 4}-1.150064{col 70}{space 3}-.5901122
{txt}{space 6}year_fe29 {c |}{col 17}{res}{space 2}  .129994{col 29}{space 2} .0846502{col 40}{space 1}    1.54{col 49}{space 3}0.125{col 57}{space 4}-.0359174{col 70}{space 3} .2959054
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{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}rho1            {txt}{c |}
{space 10}_cons {c |}{col 17}{res}{space 2} .0694927{col 29}{space 2} .0196642{col 40}{space 1}    3.53{col 49}{space 3}0.000{col 57}{space 4} .0309516{col 70}{space 3} .1080337
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}rho2            {txt}{c |}
{space 10}_cons {c |}{col 17}{res}{space 2} 1.002007{col 29}{space 2} .2347472{col 40}{space 1}    4.27{col 49}{space 3}0.000{col 57}{space 4} .5419111{col 70}{space 3} 1.462103
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}sigma           {txt}{c |}
{space 10}_cons {c |}{col 17}{res}{space 2} .6939527{col 29}{space 2} .0078384{col 40}{space 1}   88.53{col 49}{space 3}0.000{col 57}{space 4} .6785897{col 70}{space 3} .7093157
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

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